WO2011004568A1 - Image processing method for observation of fertilized eggs, image processing program, image processing device, and method for producing fertilized eggs - Google Patents

Image processing method for observation of fertilized eggs, image processing program, image processing device, and method for producing fertilized eggs Download PDF

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Publication number
WO2011004568A1
WO2011004568A1 PCT/JP2010/004328 JP2010004328W WO2011004568A1 WO 2011004568 A1 WO2011004568 A1 WO 2011004568A1 JP 2010004328 W JP2010004328 W JP 2010004328W WO 2011004568 A1 WO2011004568 A1 WO 2011004568A1
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fertilized egg
image
observation
image processing
objects
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PCT/JP2010/004328
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French (fr)
Japanese (ja)
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三村正文
佐々木秀貴
伊藤啓
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株式会社ニコン
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • G01N21/8851Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • G01N21/94Investigating contamination, e.g. dust
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/60Type of objects
    • G06V20/68Food, e.g. fruit or vegetables

Definitions

  • the present invention relates to an image processing means for automatically discriminating a fertilized egg and a foreign substance from an observation image acquired in fertilized egg observation, and a fertilized egg manufacturing method using the image processing means.
  • a culture microscope is mentioned as an example of the apparatus which observes the conditions of cultures, such as a fertilized egg (for example, refer patent document 1).
  • the culture microscope includes a culture apparatus (invecutor) that forms a suitable environment for culturing fertilized eggs, and a microscopic observation system that microscopically observes the state of the fertilized eggs in the culture container housed in the culture apparatus.
  • the observation image of the fertilized egg is acquired at regular intervals, and the user can recognize the fertilized egg by visual observation and automatically perform observation, recording, management, etc. of the fertilized egg.
  • the culture medium in the culture container contains foreign matters such as dust and bubbles in addition to the fertilized egg to be observed. Since these foreign objects may have an appearance similar to a fertilized egg, it is easy to confuse the fertilized egg with the foreign object in the observation image, and it is difficult to automatically recognize the fertilized egg with simple image processing.
  • the present invention has been made in view of the problems as described above, and provides a means for automatically discriminating a fertilized egg by discriminating a fertilized egg to be observed and other foreign matters in fertilized egg observation. Objective.
  • an observation image obtained by photographing a plurality of objects located in the observation visual field by the imaging device is acquired, and a plurality of objects imprinted in the observation image are extracted, and the observation image is extracted.
  • Calculating a plurality of feature values of an image according to the attributes of a fertilized egg for each object included in the object, and identifying a fertilized egg from the plurality of objects based on the calculated plurality of feature values An image processing method for fertilized egg observation is provided.
  • an image processing program for causing a computer to function as an image processing device that can be read by a computer and that is captured by an imaging device and acquires an image and performs image processing.
  • a step of acquiring an observation image obtained by photographing a plurality of objects located in a field of view by an imaging device, a step of extracting a plurality of objects imprinted in the observation image, and a fertilized egg for each object included in the observation image Calculating a plurality of image feature amounts according to the attributes of the image, identifying a fertilized egg from the plurality of objects based on the calculated feature amounts, and outputting an identification result for the object
  • An image processing program for observing a fertilized egg is provided.
  • an imaging device that captures a plurality of objects, a plurality of objects are extracted from observation images captured by the imaging device, and a fertilized egg is identified from the plurality of objects.
  • An image analysis unit, and an output unit that outputs an identification result determined by the image analysis unit to the outside, and the image analysis unit determines the feature amount of the image according to the attribute of the fertilized egg for each object included in the observation image
  • a fertilized egg observation image processing apparatus is provided, which is configured to identify a fertilized egg from a plurality of objects based on a plurality of calculated feature values.
  • the fertilized egg is cultured under predetermined environmental conditions, and the fertilized egg is identified from the culture container in which the fertilized egg exists using the image processing apparatus having the above-described configuration.
  • a featured fertilized egg production method is provided.
  • a fertilized egg is cultured under a predetermined environmental condition, and an observation in which a plurality of objects located in an observation field are photographed with an imaging device in a culture container in which the fertilized egg exists.
  • Acquire an image extract a plurality of objects imprinted in the observation image, calculate a plurality of image feature amounts according to the attributes of the fertilized egg for each object included in the observation image, and calculate the plurality of features.
  • a method for producing a fertilized egg characterized by identifying a fertilized egg from a plurality of objects in a culture container based on the amount.
  • the fertilized egg observation image processing method the image processing program and the image processing apparatus, and the fertilized egg manufacturing method, by image processing that discriminates an object based on a plurality of feature amounts according to the attributes of the fertilized egg.
  • the fertilized egg can be accurately identified from among a plurality of objects included in the observation image.
  • FIG. 1 It is a flowchart which shows the outline
  • (A) is a top view which shows a culture container
  • (B) is a perspective view which shows a dish. It is a figure for demonstrating the characteristic of a fertilized egg and other foreign materials. It is a figure which illustrates the condition of the outline extraction process which extracts an object. It is a figure for demonstrating the difference of the luminance value inside the outline of a fertilized egg and a foreign material.
  • FIG. 1 is a block diagram illustrating a schematic configuration of an image processing apparatus. It is a flowchart which shows the outline
  • FIGS. 2 and 3 As an example of a system to which the image processing apparatus according to the present embodiment is applied, a schematic configuration diagram and a block diagram of a culture observation system are shown in FIGS. 2 and 3, respectively.
  • the culture observation system BS roughly observes the culture chamber 2 provided in the upper part of the housing 1, the shelf-like stocker 3 that accommodates and holds a plurality of culture containers 10, and the sample in the culture container 10.
  • An observation unit 5 a transfer unit 4 for transferring the culture vessel 10 between the stocker 3 and the observation unit 5, a control unit 6 for comprehensively controlling the operation of the system, and an operation panel 7 provided with an image display device. Etc.
  • the culture room 2 is a room that forms a culture environment, and is kept sealed after the sample is charged in order to prevent environmental changes and contamination.
  • a temperature adjustment device 21 that raises and lowers the temperature in the culture chamber 2
  • a humidifier 22 that adjusts humidity
  • a gas supply device 23 that supplies a gas such as CO 2 gas or N 2 gas.
  • a circulation fan 24 for making the entire environment of the culture chamber 2 uniform, an environmental sensor 25 for detecting the temperature, humidity, carbon dioxide concentration, etc. of the culture chamber 2 are provided.
  • the operation of each device is controlled by the control unit 6, and the culture environment defined by the temperature, humidity, carbon dioxide concentration, etc. of the culture chamber 2 is maintained in a state that matches the culture conditions set on the operation panel 7.
  • the stocker 3 is formed in a shelf shape that is partitioned into a plurality of parts in the front-rear direction and the up-down direction in FIG. Each shelf has its own unique address. For example, when the longitudinal direction is A to C rows and the vertical direction is 1 to 7 rows, the A row 5 shelves are set as A-5.
  • the culture vessel 10 an appropriate one such as a flask, a dish, or a well plate is selected according to the type and purpose of the culture.
  • the fertilized egg a which is a culture is a culture medium containing pH indicators, such as phenol red It is injected into each dish 10a together with the drop D.
  • the medium drops D of about 20 ⁇ l dropped by a pipette or the like are formed (only one is shown in FIG. 4B), and the medium drop D is contained in the dish 10a.
  • each medium drop D for example, one fertilized egg a collected from the same mother at the same time for external fertilization is inserted.
  • the culture vessel 10 is assigned a code number and is stored in association with the designated address of the stocker 3.
  • the transfer unit 4 is provided inside the culture chamber 2 so as to be movable in the vertical direction and is moved up and down by the Z-axis drive mechanism.
  • the transfer unit 4 is attached to the Z stage 41 so as to be movable in the front-rear direction and by the Y-axis drive mechanism.
  • the Y stage 42 that is moved back and forth, the X stage 43 that is attached to the Y stage 42 so as to be movable in the left-right direction and is moved left and right by the X-axis drive mechanism, and the like, is supported by lifting the culture vessel 10 to the tip side of the X stage 43
  • a support arm 45 is provided.
  • the transport unit 4 has a moving range in which the support arm 45 can move between the entire shelf of the stocker 3 and the observation unit 5.
  • the X-axis drive mechanism, the Y-axis drive mechanism, and the Z-axis drive mechanism are configured by, for example, a servo motor with a ball screw and an encoder, and the operation thereof is controlled by
  • the observation unit 5 includes a first illumination unit 51 that illuminates the sample from the lower side of the sample stage 15, a second illumination unit 52 that illuminates the sample from above the sample stage 15 along the optical axis of the microscopic observation system, and a sample from below. 3, a macro observation system 54 that performs macro observation of the sample, a micro observation system 55 that performs micro observation of the sample, an image processing apparatus 100 (see FIG. 10), and the like.
  • the sample stage 15 is made of a light-transmitting material and has a transparent window 16 in the observation area.
  • the sample stage 15 is composed of a fine drive stage that can be moved in the XY direction (horizontal plane direction) and the Z direction (vertical direction) by operation control from the control unit 6, and the culture vessel 10 placed on the upper surface thereof. Is moved in the XY directions, so that the culture vessel 10 can be inserted on the optical axis of the macro observation system 54 or on the optical axis of the microscopic observation system 55.
  • the first illumination unit 51 is composed of a surface-emitting light source provided on the lower frame 1b side, and backlight-illuminates the entire culture vessel 10 from the lower side of the sample stage 15.
  • the second illumination unit 52 includes a light source such as an LED and an illumination optical system including a phase ring, a condenser lens, and the like.
  • the second illumination unit 52 is provided in the culture chamber 2 and receives light from the microscopic observation system 55 from above the sample stage 15.
  • the sample in the culture vessel 10 is illuminated along the axis.
  • the third illumination unit 53 superimposes light emitted from each of the light sources, such as a plurality of LEDs and mercury that emit light having a wavelength suitable for epi-illumination observation and fluorescence observation, on the optical axis of the microscopic observation system 55.
  • An illumination optical system composed of a beam splitter, a fluorescent filter, and the like to be disposed in the lower frame 1b located below the culture chamber 2, and the light of the microscopic observation system 55 from below the sample stage 15 The sample in the culture vessel 10 is illuminated along the axis.
  • the macro observation system 54 includes an observation optical system 54 a and an imaging device 54 c such as a CCD camera that captures an image of the sample imaged by the observation optical system 54 a and is positioned above the first illumination unit 51. Provided in the culture chamber 2.
  • the macro observation system 54 captures an entire observation image (macro image) from above the culture vessel 10 that is backlit by the first illumination unit 51.
  • the microscopic observation system 55 includes an observation optical system 55a composed of an objective lens, an intermediate zoom lens, a fluorescent filter, and the like, and an imaging device 55c such as a cooled CCD camera that takes an image of a sample imaged by the observation optical system 55a. And disposed inside the lower frame 1b.
  • the second illumination unit 52 and the microscopic observation system 55 constitute a phase difference observation microscope.
  • a plurality of objective lenses and intermediate zoom lenses are provided, and are configured to be set to a plurality of magnifications using a displacement mechanism such as a revolver or a slider (not shown in detail).
  • the microscopic observation system 55 displays the sample in the culture vessel 10 such as a phase difference image by the transmitted light of the sample illuminated by the second illumination unit 52 and a fluorescence image by the fluorescence emitted from the sample illuminated by the third illumination unit 53.
  • a microscopic observation image is taken.
  • the image processing apparatus 100 performs A / D conversion on signals input from the imaging device 54c of the macro observation system 54 and the imaging device 55c of the microscopic observation system 55, and performs various image processing to perform an entire observation image or a microscopic observation image. Image data is generated. Further, the image processing apparatus 100 performs image analysis on the image data of these observation images (entire observation image and microscopic observation image), calculates the feature amount of an object present in the image, and responds to each feature amount. Image processing such as calculating the score and determining the fertilized egg based on the total score. Specifically, the image processing apparatus 100 is constructed by executing an image processing program stored in the ROM of the control unit 6 described below. The image processing apparatus 100 will be described in detail later.
  • the control unit 6 includes a CPU 61 that executes processing, a ROM 62 that stores and stores control programs and control data of the culture observation system BS, a RAM 63 that temporarily stores observation conditions and image data, and the like. Control operation. Therefore, as shown in FIG. 3, the constituent devices of the culture chamber 2, the transport device 4, the observation unit 5, and the operation panel 7 are connected to the control unit 6.
  • the RAM 63 the environmental conditions of the culture chamber 2 according to the observation program, the observation schedule, the observation type and observation position in the observation unit 5, the observation magnification, and the like are set and stored. Further, the RAM 63 is provided with an image data storage area for recording image data photographed by the observation unit 5, and index data including the code number of the culture vessel 10 and photographing date / time are associated with the image data and recorded. Is done.
  • the operation panel 7 is provided with an operation panel 71 provided with input / output devices such as a keyboard and a switch, and a display panel 72 for displaying an operation screen, image data, and the like. An operation command or the like is input.
  • the communication unit 65 is configured in accordance with a wired or wireless communication standard, and data can be transmitted to and received from a computer or the like externally connected to the communication unit 65.
  • the CPU 61 controls the operation of each part based on the control program stored in the ROM 62 according to the setting conditions of the observation program set on the operation panel 7, and the culture vessel 10
  • the sample inside is automatically captured. That is, when the observation program is started by a panel operation on the operation panel 71 (or a remote operation via the communication unit 65), the CPU 61 reads each condition value of the environmental conditions stored in the RAM 63, and from the environment sensor 25.
  • the environmental state of the culture chamber 2 to be input is detected, and the temperature adjustment device 21, the humidifier 22, the gas supply device 23, the circulation fan 24, etc. are operated according to the difference between the condition value and the actual measurement value. Feedback control is performed on the culture environment such as temperature, humidity, and carbon dioxide concentration.
  • the CPU 61 reads the observation conditions stored in the RAM 63 and operates the X, Y, Z stages 41, 42, 43 of the transport unit 4 based on the observation schedule to observe the culture vessel 10 to be observed from the stocker 3.
  • the sample is transported to the sample stage 15 of the unit 5 and observation by the observation unit 5 is started.
  • the observation set in the observation program is macro observation
  • the culture vessel 10 transported from the stocker 3 by the transport unit 4 is positioned on the optical axis of the macro observation system 54 and placed on the sample stage 15.
  • the light source of the first illumination unit 51 is turned on, and the entire observation image is taken by the imaging device 54c from above the culture vessel 10 that is backlit.
  • the signal input from the imaging device 54c to the control unit 6 is processed by the image processing device 100 to generate a whole observation image, and the image data is stored in the image data storage area of the RAM 63 together with index data such as the shooting date and time. Is done.
  • the observation set in the observation program is micro observation of the sample at a specific position in the culture vessel 10
  • the specific position in the culture vessel 10 that has been transported by the transport unit 4 is indicated by the light of the microscopic observation system 55.
  • a signal photographed by the imaging device 55c and inputted to the control unit 6 is processed by the image processing device 100 to generate a microscopic observation image (phase difference image, fluorescent image, etc.), and the image data is an index such as a photographing date / time.
  • the CPU 61 sequentially executes the above-described whole observation image photographing and microscopic observation image photographing based on the observation schedule set in the observation program.
  • the image data stored in the RAM 63 is read from the RAM 63 in response to an image display command input from the operation panel 71. For example, an entire observation image, a microscopic observation image at a specified time, an analysis result of image analysis, and the like are displayed on the display panel 72. Is displayed.
  • the image processing apparatus 100 performs observation of the growth state and the like of the fertilized egg in the culture container 10, so that the fertilized egg to be observed and other garbage or the like It has a function of discriminating foreign substances (asymmetrical substances) such as bubbles. That is, the medium drop D in the culture container 10 is mixed with other objects such as dust and bubbles in addition to the fertilized egg, which may have a similar appearance to the fertilized egg and are confused in observation. Since it is easy, the image processing apparatus 100 has a function of automatically discriminating a fertilized egg to be observed by discriminating a fertilized egg from other foreign substances.
  • foreign substances asymmetrical substances
  • each of the fertilized egg, dust, oil particles, and bubbles has the following characteristics.
  • a fertilized egg has a feature that a contour (a zona pellucida) is present in the contour, the outer shape is spherical, and an egg cell is present in the contour.
  • a contour a zona pellucida
  • the dust has the characteristics that there is no transparent band in the outline, halo appears in the outline in the phase difference image, and the structure inside the outline is indefinite.
  • the oil particles and bubbles have the characteristics that the contour portion is dark and the inside is bright, the outer shape is spherical or elliptical, and the structure inside the contour is small.
  • the fertilized egg has a higher degree of circularity than the foreign object, the fertilized egg has a zona pellucida in the contour, the internal structure of the fertilized egg (Egg cells) are always present.
  • the image processing method for observing a fertilized egg by the image processing apparatus 100 acquires images obtained by photographing an object located in the observation field of view of the observation unit 5 with an imaging device, and images of the objects (objects) imprinted in these images. As a result of calculating the above three feature quantities and scoring (score calculation) based on these feature quantities, it is determined that the fertilized egg is the one that has the highest score among these three feature quantities. Configured. In the following, this image processing method will be described from the basic concept. In the following description, a case where a fertilized egg is observed based on a phase contrast image (microscopic observation image) photographed by a phase contrast microscope configured by the second illumination unit 52, the microscopic observation system 55, and the like will be exemplified. .
  • FIG. 6 is a diagram showing a state of the contour extraction process. A plurality of objects are extracted from the observation image (A) photographed and acquired by the imaging device 55c as in the binary image shown in (B).
  • Each segmented object (binary image) is labeled to give a unique label.
  • the area of the object (area surrounded by the extracted contour) is calculated together with labeling. Therefore, the size of an image of a fertilized egg (for example, a diameter of about 100 ⁇ m in the case of a human fertilized egg) is almost determined by the image acquisition conditions (observation magnification, etc.), and is therefore subject to discrimination.
  • An upper limit value and a lower limit value of a possible area of the object are set in advance, and an object outside the set range is excluded from a labeling target (fertilized egg discrimination candidate).
  • Contour circularity Utilizing the fact that the fertilized egg to be observed has a spherical shape (the cross section is close to a perfect circle), the circularity of each object is used as the feature amount 1.
  • the degree of circularity of an object is a scale for determining the degree of circularity of an object. For example, after determining the center of gravity of each object in the image plane and detecting the edge indicating the outline of each object, Then, the shortest distance and the longest distance from the center of gravity to the contour are calculated and obtained from the following equation ( ⁇ ).
  • Contour circularity (shortest distance from centroid to contour) / (longest distance from centroid to contour)... ( ⁇ )
  • the circularity obtained in this way is calculated as a numerical value within the range of 0 to 1, and indicates a numerical value closer to 1 as the distance from the center of gravity to the contour becomes a uniform circular shape. That is, an object having a circularity close to 1 has a higher score as a fertilized egg (highly likely to be a fertilized egg).
  • the degree of circularity of the contour the complexity of the shape (contour circumference 2 / area), the eccentricity using the second moment, and the like are exemplified.
  • Brightness difference between outside and inside contour A transparent thin film called a zona pellucida exists in the outline of the fertilized egg to be observed.
  • the difference in luminance value between the outside and inside of the contour is small.
  • the inside of the outline of oil particles and bubbles is darker than the background outside, and in the phase contrast image, the inside of the outline of dust tends to be brightened by halo, so the difference in brightness value between the outside and inside of the outline is large.
  • an average luminance value (for example, 8-bit gradation) between the outer ring zone (outer ring zone) and the inner ring zone (inner ring zone) of the contour in the object is used.
  • the difference in brightness is obtained by the following equation ( ⁇ ) using the difference of 0 to 255).
  • Brightness difference between outside and inside contour (255 ⁇
  • FIG. 8 is a diagram illustrating the state of mask generation processing when calculating the average luminance value of the outer annular zone and the inner annular zone.
  • the images (labeling) obtained by the above-described processing are illustrated.
  • a binary image is used.
  • the binary image (label image) of each object is subjected to expansion and contraction processing, and two types of mask images (expansion mask M1 and contraction mask M2) showing the expanded and contracted regions are prepared for each object. To do.
  • the difference between the areas of the two mask images M1 and M2 and the original original label image is calculated in a state where the centers of gravity coincide with each other, and these substantially ring-shaped difference images are used as the outline of the object.
  • Texture feature amount inside contour Egg cells are contained inside the fertilized egg to be observed, and it is considered that a texture (internal texture) exists on the image. On the other hand, oil grains and bubbles do not have an internal structure. Therefore, by detecting the internal structure of each object, it is possible to differentiate a fertilized egg from oil particles and bubbles.
  • image region As for the region of the object for detecting the internal structure, as shown in FIG. 9, using the contraction mask M2 generated by the above processing, an image region corresponding to its contraction mask M2 in the observation image (hereinafter, referred to as “image region”). Also referred to as “shrink mask area”.
  • an average value of edge strength (gradation change strength) in the entire image in the contraction mask region is used (see the following equation ( ⁇ )).
  • the edge strength is obtained on a pixel basis by a convolution operation in which a differential filter such as a Laplacian filter represented by, for example, a 3 ⁇ 3 matrix or a 5 ⁇ 5 matrix is applied to the image of the contraction mask region of each object.
  • Texture feature amount inside contour total edge intensity in shrink mask area / number of pixels in shrink mask area ( ⁇ )
  • the number of pixels included in the contraction mask region is used to obtain the average edge intensity. Then, an average value obtained by calculating the edge strength in the contraction mask area for each object in units of pixels in this way is obtained as the texture feature quantity inside the object, which is the feature quantity 3 by the number of pixels in the contraction mask area. .
  • the value (average value of edge strength) itself is not used, but the ratio (edge strength) to the maximum value of the average value of edge strength in all labeled objects. Average value / maximum value of edge strength average value) is used. This is because, for the feature amount 3, the edge strength changes extremely depending on the contrast of the image, so that it is meaningless to provide a certain threshold value. Normalize between images of all objects to remove. Further, when there is no internal texture (for example, when only bubbles are captured), the edge strength originally appears to be small as a whole, but by performing normalization, the feature amount 3 is large for all objects. Since there is a risk of becoming too much, a permissible value is set in advance, and when the maximum value of the edge strength average value falls below this permissible value, all of the feature amount 3 is rejected.
  • the lower limit value is set to 0, and the upper limit value is less than the above allowable value. Is set to 0, and scoring of the feature amount 3 is not performed.
  • the feature quantities normalized and converted to a common scale are set as scores 1, 2, and 3, and a total score that is the sum of them is calculated.
  • Overall score Score 1 + Score 2 + Score 3
  • the object having the maximum total score in the medium drop D is determined as a fertilized egg.
  • a score indicating the degree of fertility is calculated based on the feature amount, and the fertilized egg can be recognized clearly by separating the fertilized egg from the foreign substance based on the height of this score.
  • a fertilized egg based on each score in addition to ranking based on the total score as a simple sum of the scores 1 to 3, for example, a total score obtained by changing the weights for the scores 1 to 3 respectively.
  • Calculate and determine the fertilized egg rank each score 1 to 3 and judge that the object with the lowest total rank is a fertilized egg, Among them, a method of determining that the highest score is 3 is a fertilized egg.
  • the structure which recognizes that a total score exceeded the threshold value as a fertilized egg may be sufficient.
  • FIG. 1 is a flowchart showing an outline of processing in the image processing program GP for fertilized egg discrimination
  • FIG. 10 is a block diagram showing a schematic configuration of an image processing apparatus 100 that executes image processing for fertilized egg discrimination.
  • the image processing apparatus 100 includes an image storage unit 110 that acquires and stores an observation image in which a fertilized egg to be observed is captured by the imaging device 55c, and an object that analyzes the observation image and is imprinted on the observation image.
  • An image analysis unit 120 that determines whether or not the image analysis unit 120, a feature amount storage unit 130 that stores the three feature amounts calculated for each object by the image analysis unit 120 in association with labels assigned to the objects,
  • An output unit 140 that outputs the determination result analyzed by the image analysis unit 120 to the outside, and outputs, for example, to the display panel 72 the identification result as to whether or not the object determined by the image analysis unit 120 is a fertilized egg Configured to be displayed.
  • the image processing apparatus 100 is configured such that an image processing program GP preset and stored in the ROM 62 is read by the CPU 61 and processing based on the image processing program GP is sequentially executed by the CPU 61.
  • the fertilized egg in the culture vessel 10 designated every predetermined time is observed according to the observation conditions set in the observation program.
  • the CPU 61 operates each stage of the transport unit 4 to transport the culture vessel 10 to be observed from the stocker 3 to the observation unit 5 (in this embodiment, it is arranged on the optical axis of the microscopic observation system 55). Then, an observation image (phase difference image) by the microscopic observation system 55 using the second illumination unit 52 is photographed by the imaging device 55c.
  • the image processing apparatus 100 first acquires an observation image (phase difference image) taken by the imaging device 55c in step S1, and acquires the acquired observation image such as the code number, the observation position, and the observation time of the culture vessel 10. It is stored in the image storage unit 110 together with the index data.
  • an observation image phase difference image
  • the acquired observation image such as the code number, the observation position, and the observation time of the culture vessel 10. It is stored in the image storage unit 110 together with the index data.
  • step S2 contour extraction processing such as the LevelSet method is performed on the observation image (phase difference image) acquired from the imaging device 55c in the image analysis unit 120, and as shown in FIG. Objects included in are extracted.
  • each object of the observation image (binary image) from which the contour is extracted is labeled.
  • an appropriate range (upper and lower limit values) is set as the area of the image of the fertilized egg, and the area is calculated for each object region from which the contour is extracted, and has a region area that exceeds this set range.
  • Objects are excluded from the fertilized egg discrimination candidates, and only objects having a region area that falls within the set range are given unique labels as fertilized egg discrimination candidates.
  • the image analysis unit 120 calculates the circularity of the contour, the brightness difference between the outside and inside of the contour, and the texture feature amount inside the contour as the image feature amount of the fertilized egg (step S4).
  • the image analysis unit 120 records the unique label assigned to each object and the three feature amounts calculated below in the feature amount storage unit 130 in association with each other.
  • the image analysis unit 120 calculates the center of gravity of the object from the label image, and the longest distance from the center of gravity to the contour is the distance from the center of gravity to the contour (edge). The ratio of the shortest distance to is calculated as the circularity of the contour (step S11).
  • the image analysis unit 120 In the processing flow F20 for obtaining the luminance difference (feature value 2) between the outer side and the inner side of the contour, the image analysis unit 120 generates two mask images of the expansion mask M1 and the contraction mask M2 for each object from the label image. Thus, difference masks M3 and M4 are respectively generated from the difference between the two mask images M1 and M2 and the original label image. Then, the average of the luminance values in the regions corresponding to the region of the outer differential mask M3 and the region of the inner differential mask M4 in the observation image captured by the imaging device 55c is obtained, and the contour is calculated using the above equation ( ⁇ ). The brightness difference between the outside and the inside is calculated (step S21).
  • the contraction mask M2 generated in step S21 is used, and a differential filter is applied to the observation image captured by the imaging device 55c, so that the contraction mask M2 is applied.
  • the average edge strength in the corresponding region is calculated as the texture feature amount inside the contour (step S31).
  • the image analysis unit 120 compares each feature quantity with a preset threshold value for each feature quantity calculated for each object and recorded in the feature quantity storage unit 130, and rejects each threshold value. Perform (Steps S12, S22, S32). Only objects having three feature quantities that satisfy the threshold are regarded as fertilized egg candidates. As described above, the texture feature amount (feature amount 3) inside the contour is treated as a ratio to the maximum value of the average luminance in all objects so that the feature amount does not change greatly due to the influence of contrast.
  • step S5 normalization of the scale is performed on each feature amount with a predetermined upper and lower limit range as a full scale
  • each feature amount converted into a common scale is set as scores 1, 2, and 3. Obtained (step S6).
  • step S7 a total score composed of the sum of scores 1, 2, and 3 is calculated for each object, and the total scores are sorted (rearranged in descending order of scores).
  • the object with the label having the maximum total score is recognized as a fertilized egg to be observed (step S8).
  • step S9 a determination result that the object with this label is a fertilized egg is output from the output unit 140.
  • the determination result output from the output unit 130 is displayed on the display panel 72 of the operation panel 7, and a display indicating a fertilized egg is displayed on the object having the highest overall score in the observation image.
  • a symbol indicating that the egg is a fertilized egg for example, “J”
  • the fertilized egg is displayed with a different hue or brightness
  • a foreign object is displayed.
  • the interface is exemplified such that a fertilized egg and other foreign matters are discriminated and displayed by, for example, painting and displaying an image from which foreign matters have been removed.
  • the discrimination data output from the output unit 140 is transmitted to a computer or the like externally connected via the communication unit 65 to display the same image or observe the growth state of the fertilized egg. For use as basic data.
  • the observer refers to the image displayed on the display panel 72 and the image displayed on a monitor such as an externally connected computer, so that each of the observers who are observing (or that has already finished obtaining the observed image) It is possible to immediately determine whether or not the object included in the image is a fertilized egg. In addition, by using the data in which the fertilized egg and other foreign matters are discriminated in this way, the growth state of the fertilized egg can be efficiently observed.
  • step S110 the fertilized egg is injected into the culture container 10 (dish 10a) together with the medium drop D, and the culture container 10 is stored in the culture chamber 2 maintained at environmental conditions suitable for culturing the fertilized egg. Then, fertilized eggs are cultured under the environmental conditions. The environmental conditions are adjusted in the control unit 6 according to the culture environment of the fertilized egg such as the temperature, humidity, and carbon dioxide concentration in the culture chamber 2.
  • step S120 as the observation of the fertilized egg in the culture container 10, the above-described image processing steps S1 to S9 (see FIG. 1) are executed, and a fertilized egg is selected from a plurality of objects copied in the observation image. Identify. At this time, one fertilized egg is identified for each medium drop D in the culture vessel 10 (dish 10a).
  • a plurality of fertilized eggs identified for each medium drop D are selected based on a predetermined selection criterion.
  • a selection standard for a fertilized egg the grade of the fertilized egg is determined based on the timing of cleavage, the shape of the blastomere, and the like, and a good one satisfying this selection standard is selected. For example, it is performed based on whether or not the timing at which cleavage occurs in all egg cells in the egg is in the same period as having passed through a good growth state. That is, regarding the cleavage of normal fertilized eggs, cells of the same generation divide at almost the same timing, and only cells of the same generation exist in the embryo. On the other hand, regarding the cleavage of an abnormal fertilized egg, even when cells are of the same generation, the division timing is shifted, and cells of different generations are mixed in the embryo.
  • the selected fertilized eggs (good fertilized eggs that have grown to a state called a blastocyst) are collected and stored frozen, for example, in liquid nitrogen at minus 196 ° C.
  • the fertilized egg (blastocyst) is returned to the mother (embryo transfer) at a predetermined cycle.
  • the fertilized eggs to be cultured may be fertilized eggs such as humans, cows, horses, pigs and mice.
  • the fertilized egg may be stored in a blastocyst state or may be stored in a divided phase (4-cell stage embryo, 8-cell stage embryo).
  • the image processing method and image processing apparatus 100 for observing a fertilized egg configured by executing the image processing program GP, and the method for manufacturing a fertilized egg
  • the image processing method and image processing apparatus 100 for observing a fertilized egg configured by executing the image processing program GP, and the method for manufacturing a fertilized egg
  • the processing method for recognizing a fertilized egg based on the three feature amounts of the circularity of the contour, the luminance difference between the outside and inside of the contour, and the texture feature amount inside the contour is illustrated.
  • the present invention is not limited to this embodiment, and a method for recognizing a fertilized egg based on other feature quantities (such as shape feature quantities and texture feature quantities), and four additional feature quantities are added. Even when applied to a method for recognizing a fertilized egg based on the five feature quantities, the same effect can be obtained. For example, based on an image with a relatively low observation magnification, the brightness value of the outline of the object, the variance value of the brightness inside the object, and the like are used as new feature values, replacing one of the above three feature values.
  • the configuration may be configured as four or five feature amounts in addition to the above three feature amounts.
  • the observation magnification is configured to be variable according to lens settings such as an objective lens, and is obtained from the observation image.
  • the feature amount includes a feature amount that appears more prominently in a low-magnification image (low-magnification phase difference image) and a feature amount that appears more prominently in a high-magnification image (high-magnification phase difference image).
  • the three feature quantities exemplified in the present embodiment are preferably obtained based on a high-resolution high-magnification image so that numerical values and textures can be detected, and the brightness inside the contour as the new feature quantities described above.
  • the value is preferably obtained based on a low-magnification image in which a luminance change is noticeable.
  • the high-magnification image is an image having an observation magnification of 10 or 20 times, for example
  • the low-magnification image is an image having an observation magnification of about 2 times, for example, the size of a fertilized egg, egg cell, embryo, or the like to be observed
  • An appropriate magnification can be used according to the above.
  • the configuration for identifying and processing one fertilized egg a injected into the medium drop D is exemplified, but the present invention is not limited to this embodiment, You may comprise so that all the several fertilized eggs a inject
  • a threshold value for identifying a fertilized egg is set in advance for the total score, and an object exceeding this threshold value is determined to be a fertilized egg, or the fertilized egg a injected into the medium drop D
  • a configuration may be adopted in which only the number is set in advance, and an object having a higher overall score corresponding to the number is recognized as a fertilized egg.
  • BS culture observation system GP image processing program a fertilized egg 5 observation unit 6 control unit 7 operation panel 54 macro observation system 54c imaging device 55 micro observation system 55c imaging device 61 CPU 62 ROM 63 RAM 100 Image Processing Device 120 Image Analysis Unit 140 Output Unit

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Abstract

Disclosed is a means for automatically recognizing fertilized eggs to be observed by distinguishing fertilized eggs from contaminants during observation of fertilized eggs. A computer executes the following steps: a step (S1) of obtaining an observation image depicting a plurality of objects positioned within a field of view from an imaging device; a step (S2) of extracting the plurality of objects that are depicted in the observation image; steps (S11, S21, S31) of calculating a plurality of feature values, corresponding to the attributes of fertilized eggs, in the image for every object included in the observation image; a step (S8) of identifying fertilized eggs among the plurality of objects on the basis of the calculated feature values; and a step (9) of outputting the identification results for the objects.

Description

受精卵観察の画像処理方法、画像処理プログラム及び画像処理装置、並びに受精卵の製造方法Fertilized egg observation image processing method, image processing program, image processing apparatus, and fertilized egg manufacturing method
 本発明は、受精卵観察において取得された観察画像から受精卵と異物とを自動判別する画像処理手段、及びこれを利用した受精卵の製造方法に関する。 The present invention relates to an image processing means for automatically discriminating a fertilized egg and a foreign substance from an observation image acquired in fertilized egg observation, and a fertilized egg manufacturing method using the image processing means.
 近年、生殖補助医療技術(ART)の発展に伴い、体外受精による受精卵を培養しながらその生育状態を観察することが行われている。受精卵などの培養物の状況を観察する装置の例として、培養顕微鏡が挙げられる(例えば、特許文献1を参照)。培養顕微鏡は、受精卵の培養に好適な環境を形成する培養装置(インベキュータ)と、培養装置に収容された培養容器内の受精卵の状態を顕微観察する顕微観察系とを備え、予め設定された一定時間ごとに受精卵の観察画像を取得し、ユーザが受精卵を目視により認識した上で、受精卵の生育状態の観察、記録、管理等を自動で行うことができるように構成される。 In recent years, with the development of assisted reproduction technology (ART), it has been observed to observe the growth state of cultured fertilized eggs by in vitro fertilization. A culture microscope is mentioned as an example of the apparatus which observes the conditions of cultures, such as a fertilized egg (for example, refer patent document 1). The culture microscope includes a culture apparatus (invecutor) that forms a suitable environment for culturing fertilized eggs, and a microscopic observation system that microscopically observes the state of the fertilized eggs in the culture container housed in the culture apparatus. The observation image of the fertilized egg is acquired at regular intervals, and the user can recognize the fertilized egg by visual observation and automatically perform observation, recording, management, etc. of the fertilized egg. The
特開2004-229619号公報JP 2004-229619 A
 このような装置において、培養容器中の培地には観察対象の受精卵以外に、ゴミや気泡等の異物が混入している。それら異物は受精卵と似た外観を持つことがあるため、観察画像において受精卵と異物とを混同しやすく、単純な画像処理では受精卵の自動認識をすることは困難であった。 In such an apparatus, the culture medium in the culture container contains foreign matters such as dust and bubbles in addition to the fertilized egg to be observed. Since these foreign objects may have an appearance similar to a fertilized egg, it is easy to confuse the fertilized egg with the foreign object in the observation image, and it is difficult to automatically recognize the fertilized egg with simple image processing.
 本発明は、上記のような課題に鑑みてなされたものであり、受精卵観察において観察対象の受精卵とそれ以外の異物とを判別し、受精卵を自動で認識する手段を提供することを目的とする。 The present invention has been made in view of the problems as described above, and provides a means for automatically discriminating a fertilized egg by discriminating a fertilized egg to be observed and other foreign matters in fertilized egg observation. Objective.
 本発明を例示する第1の態様に従えば、観察視野内に位置する複数の物体を撮像装置により撮影した観察画像を取得し、観察画像に写し込まれた複数の物体を抽出し、観察画像に含まれる物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、算出された複数の特徴量に基づいて、複数の物体の中から受精卵を識別することを特徴とする受精卵観察の画像処理方法が提供される。 According to the first aspect exemplifying the present invention, an observation image obtained by photographing a plurality of objects located in the observation visual field by the imaging device is acquired, and a plurality of objects imprinted in the observation image are extracted, and the observation image is extracted. Calculating a plurality of feature values of an image according to the attributes of a fertilized egg for each object included in the object, and identifying a fertilized egg from the plurality of objects based on the calculated plurality of feature values An image processing method for fertilized egg observation is provided.
 本発明を例示する第2の態様に従えば、コンピュータにより読み取り可能であり、撮像装置により撮影されて画像を取得して画像処理する画像処理装置としてコンピュータを機能させるための画像処理プログラムであって、撮像装置により視野内に位置する複数の物体を撮影した観察画像を取得するステップと、観察画像に写し込まれた複数の物体を抽出するステップと、観察画像に含まれる物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出するステップと、算出された複数の特徴量に基づいて、複数の物体の中から受精卵を識別するステップと、物体に対する識別結果を出力するステップとをコンピュータに実現させることを特徴とする受精卵観察の画像処理プログラムが提供される。 According to a second aspect exemplifying the present invention, there is provided an image processing program for causing a computer to function as an image processing device that can be read by a computer and that is captured by an imaging device and acquires an image and performs image processing. A step of acquiring an observation image obtained by photographing a plurality of objects located in a field of view by an imaging device, a step of extracting a plurality of objects imprinted in the observation image, and a fertilized egg for each object included in the observation image Calculating a plurality of image feature amounts according to the attributes of the image, identifying a fertilized egg from the plurality of objects based on the calculated feature amounts, and outputting an identification result for the object An image processing program for observing a fertilized egg is provided.
 本発明を例示する第3の態様に従えば、複数の物体を撮影する撮像装置と、撮像装置により撮影された観察画像から複数の物体を抽出し、複数の物体の中から受精卵を識別する画像解析部と、画像解析部により判断された識別結果を外部に出力する出力部とを備え、画像解析部が、観察画像に含まれる物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、算出された複数の特徴量に基づいて、複数の物体の中から受精卵を識別するように構成したことを特徴とする受精卵観察の画像処理装置が提供される。 According to the third aspect exemplifying the present invention, an imaging device that captures a plurality of objects, a plurality of objects are extracted from observation images captured by the imaging device, and a fertilized egg is identified from the plurality of objects. An image analysis unit, and an output unit that outputs an identification result determined by the image analysis unit to the outside, and the image analysis unit determines the feature amount of the image according to the attribute of the fertilized egg for each object included in the observation image A fertilized egg observation image processing apparatus is provided, which is configured to identify a fertilized egg from a plurality of objects based on a plurality of calculated feature values.
 本発明を例示する第4の態様に従えば、所定の環境条件で受精卵を培養し、受精卵が存在する培養容器中から、上記構成の画像処理装置を用いて受精卵を識別することを特徴とする受精卵の製造方法が提供される。 According to the fourth aspect illustrating the present invention, the fertilized egg is cultured under predetermined environmental conditions, and the fertilized egg is identified from the culture container in which the fertilized egg exists using the image processing apparatus having the above-described configuration. A featured fertilized egg production method is provided.
 本発明を例示する第5の態様に従えば、所定の環境条件で受精卵を培養し、受精卵が存在する培養容器中において、観察視野内に位置する複数の物体を撮像装置により撮影した観察画像を取得し、観察画像に写し込まれた複数の物体を抽出し、観察画像に含まれる物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、算出された複数の特徴量に基づいて、培養容器中の複数の物体の中から受精卵を識別することを特徴とする受精卵の製造方法が提供される。 According to the fifth aspect illustrating the present invention, a fertilized egg is cultured under a predetermined environmental condition, and an observation in which a plurality of objects located in an observation field are photographed with an imaging device in a culture container in which the fertilized egg exists. Acquire an image, extract a plurality of objects imprinted in the observation image, calculate a plurality of image feature amounts according to the attributes of the fertilized egg for each object included in the observation image, and calculate the plurality of features There is provided a method for producing a fertilized egg characterized by identifying a fertilized egg from a plurality of objects in a culture container based on the amount.
 このような受精卵観察の画像処理方法、画像処理プログラム及び画像処理装置、並びに受精卵の製造方法によれば、受精卵の属性に応じた複数の特徴量に基づいて物体を判別する画像処理により、観察画像に含まれる複数の物体の中から受精卵を的確に識別することが可能になる。 According to the fertilized egg observation image processing method, the image processing program and the image processing apparatus, and the fertilized egg manufacturing method, by image processing that discriminates an object based on a plurality of feature amounts according to the attributes of the fertilized egg. The fertilized egg can be accurately identified from among a plurality of objects included in the observation image.
画像処理プログラムの概要を示すフローチャートである。It is a flowchart which shows the outline | summary of an image processing program. 本発明の適用例として示す培養観察システムの概要構成図である。It is a general | schematic block diagram of the culture observation system shown as an example of application of this invention. 上記培養観察システムのブロック図である。It is a block diagram of the said culture observation system. (A)は培養容器を示す平面図であり、(B)はディッシュを示す斜視図である。(A) is a top view which shows a culture container, (B) is a perspective view which shows a dish. 受精卵とそれ以外の異物との特徴を説明するための図である。It is a figure for demonstrating the characteristic of a fertilized egg and other foreign materials. オブジェクトの抽出を行う輪郭抽出処理の状況を例示する図である。It is a figure which illustrates the condition of the outline extraction process which extracts an object. 受精卵と異物との輪郭内側の輝度値の違いを説明するための図である。It is a figure for demonstrating the difference of the luminance value inside the outline of a fertilized egg and a foreign material. 外輪帯及び内輪帯の平均輝度値を算出するときのマスク生成処理の状況を例示した図である。It is the figure which illustrated the condition of the mask production | generation process when calculating the average luminance value of an outer ring zone and an inner ring zone. 観察画像における収縮マスク領域を説明するための図である。It is a figure for demonstrating the contraction mask area | region in an observation image. 画像処理装置の概要構成を示すブロック図である。1 is a block diagram illustrating a schematic configuration of an image processing apparatus. 受精卵の製造方法の概要を示すフローチャートである。It is a flowchart which shows the outline | summary of the manufacturing method of a fertilized egg.
 以下、本発明を実施するための形態について、図面を参照しながら説明する。本実施形態に係る画像処理装置を適用したシステムの一例として、培養観察システムの概要構成図及びブロック図を、それぞれ図2及び図3に示す。 Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. As an example of a system to which the image processing apparatus according to the present embodiment is applied, a schematic configuration diagram and a block diagram of a culture observation system are shown in FIGS. 2 and 3, respectively.
 培養観察システムBSは、大別的には、筐体1の上部に設けられた培養室2と、複数の培養容器10を収容保持する棚状のストッカー3と、培養容器10内の試料を観察する観察ユニット5と、培養容器10をストッカー3と観察ユニット5との間で搬送する搬送ユニット4と、システムの作動を統括的に制御する制御ユニット6と、画像表示装置を備えた操作盤7などから構成される。 The culture observation system BS roughly observes the culture chamber 2 provided in the upper part of the housing 1, the shelf-like stocker 3 that accommodates and holds a plurality of culture containers 10, and the sample in the culture container 10. An observation unit 5, a transfer unit 4 for transferring the culture vessel 10 between the stocker 3 and the observation unit 5, a control unit 6 for comprehensively controlling the operation of the system, and an operation panel 7 provided with an image display device. Etc.
 培養室2は、培養環境を形成する部屋であり、環境変化やコンタミネーションを防止するためサンプル投入後は密閉状態に保持される。培養室2に付随して、培養室2内の温度を昇温・降温させる温度調整装置21、湿度を調整する加湿器22、COガスやNガス等のガスを供給するガス供給装置23、培養室2全体の環境を均一化させるための循環ファン24、培養室2の温度や湿度、二酸化炭素濃度等を検出する環境センサ25などが設けられている。各機器の作動は制御ユニット6により制御され、培養室2の温度や湿度、二酸化炭素濃度等により規定される培養環境が、操作盤7において設定された培養条件に合致した状態に維持される。 The culture room 2 is a room that forms a culture environment, and is kept sealed after the sample is charged in order to prevent environmental changes and contamination. Along with the culture chamber 2, a temperature adjustment device 21 that raises and lowers the temperature in the culture chamber 2, a humidifier 22 that adjusts humidity, and a gas supply device 23 that supplies a gas such as CO 2 gas or N 2 gas. A circulation fan 24 for making the entire environment of the culture chamber 2 uniform, an environmental sensor 25 for detecting the temperature, humidity, carbon dioxide concentration, etc. of the culture chamber 2 are provided. The operation of each device is controlled by the control unit 6, and the culture environment defined by the temperature, humidity, carbon dioxide concentration, etc. of the culture chamber 2 is maintained in a state that matches the culture conditions set on the operation panel 7.
 ストッカー3は、図2における紙面直行の前後方向、及び上下方向にそれぞれ複数に仕切られた棚状に形成されている。各棚にはそれぞれ固有の番地が設定されており、例えば前後方向をA~C列、上下方向を1~7段とした場合に、A列5段の棚がA-5のように設定される。 The stocker 3 is formed in a shelf shape that is partitioned into a plurality of parts in the front-rear direction and the up-down direction in FIG. Each shelf has its own unique address. For example, when the longitudinal direction is A to C rows and the vertical direction is 1 to 7 rows, the A row 5 shelves are set as A-5. The
 培養容器10は、培養物の種類や目的等に応じてフラスコやディッシュ、ウェルプレートなど適宜なものが選択され、本実施形態では、図4(A)に示すように、直径約35mmの5つのディッシュ10aと、ディッシュ10aを保持するホルダ10bとを備えた構成を例示しており、図4(B)に示すように、培養物たる受精卵aは、フェノールレッドなどのpH指示薬が入った培地ドロップDとともに各ディッシュ10aに注入される。ディッシュ10aの底面には、ピペット等により滴下された20μl程度の培地ドロップDが1~複数個形成されており(図4(B)では1個のみを図示)、培地ドロップDはディッシュ10a内において無色透明のミネラルオイルOによって浸された状態となっている。それぞれの培地ドロップD内には、例えば対外受精のために同一母体から同時期に採卵された受精卵aが1個ずつ挿入されている。また、培養容器10にはコード番号が付与され、ストッカー3の指定番地に対応づけて収容される。 As the culture vessel 10, an appropriate one such as a flask, a dish, or a well plate is selected according to the type and purpose of the culture. In this embodiment, as shown in FIG. The structure provided with the dish 10a and the holder 10b which hold | maintains the dish 10a is illustrated, and as shown to FIG. 4 (B), the fertilized egg a which is a culture is a culture medium containing pH indicators, such as phenol red It is injected into each dish 10a together with the drop D. On the bottom surface of the dish 10a, one or more medium drops D of about 20 μl dropped by a pipette or the like are formed (only one is shown in FIG. 4B), and the medium drop D is contained in the dish 10a. It is in a state immersed in colorless and transparent mineral oil O. In each medium drop D, for example, one fertilized egg a collected from the same mother at the same time for external fertilization is inserted. The culture vessel 10 is assigned a code number and is stored in association with the designated address of the stocker 3.
 搬送ユニット4は、培養室2の内部に上下方向に移動可能に設けられてZ軸駆動機構により昇降されるZステージ41、Zステージ41に前後方向に移動可能に取り付けられてY軸駆動機構により前後移動されるYステージ42、Yステージ42に左右方向に移動可能に取り付けられてX軸駆動機構により左右移動されるXステージ43などからなり、Xステージ43の先端側に培養容器10を持ち上げ支持する支持アーム45が設けられている。搬送ユニット4は、支持アーム45がストッカー3の全棚と観察ユニット5との間を移動可能な移動範囲を有して構成される。X軸駆動機構、Y軸駆動機構、Z軸駆動機構は、例えばボールネジとエンコーダ付きのサーボモータにより構成され、その作動が制御ユニット6により制御される。 The transfer unit 4 is provided inside the culture chamber 2 so as to be movable in the vertical direction and is moved up and down by the Z-axis drive mechanism. The transfer unit 4 is attached to the Z stage 41 so as to be movable in the front-rear direction and by the Y-axis drive mechanism. The Y stage 42 that is moved back and forth, the X stage 43 that is attached to the Y stage 42 so as to be movable in the left-right direction and is moved left and right by the X-axis drive mechanism, and the like, is supported by lifting the culture vessel 10 to the tip side of the X stage 43 A support arm 45 is provided. The transport unit 4 has a moving range in which the support arm 45 can move between the entire shelf of the stocker 3 and the observation unit 5. The X-axis drive mechanism, the Y-axis drive mechanism, and the Z-axis drive mechanism are configured by, for example, a servo motor with a ball screw and an encoder, and the operation thereof is controlled by the control unit 6.
 観察ユニット5は、試料台15の下側から試料を照明する第1照明部51、顕微観察系の光軸に沿って試料台15の上方から試料を照明する第2照明部52、下方から試料を照明する第3照明部53、試料のマクロ観察を行うマクロ観察系54、試料のミクロ観察を行う顕微観察系55、及び画像処理装置100(図10を参照)などから構成される。試料台15は、透光性を有する材質で構成されるとともに観察領域に透明な窓部16が設けられている。また、試料台15は、制御ユニット6からの作動制御によりXY方向(水平面内方向)およびZ方向(上下方向)に移動可能な微細駆動ステージからなり、その上面部に載置された培養容器10をXY方向に移動させることにより、培養容器10をマクロ観察系54の光軸上へ挿入したり、顕微観察系55の光軸上へ挿入したりすることが可能になっている。 The observation unit 5 includes a first illumination unit 51 that illuminates the sample from the lower side of the sample stage 15, a second illumination unit 52 that illuminates the sample from above the sample stage 15 along the optical axis of the microscopic observation system, and a sample from below. 3, a macro observation system 54 that performs macro observation of the sample, a micro observation system 55 that performs micro observation of the sample, an image processing apparatus 100 (see FIG. 10), and the like. The sample stage 15 is made of a light-transmitting material and has a transparent window 16 in the observation area. The sample stage 15 is composed of a fine drive stage that can be moved in the XY direction (horizontal plane direction) and the Z direction (vertical direction) by operation control from the control unit 6, and the culture vessel 10 placed on the upper surface thereof. Is moved in the XY directions, so that the culture vessel 10 can be inserted on the optical axis of the macro observation system 54 or on the optical axis of the microscopic observation system 55.
 第1照明部51は、下部フレーム1b側に設けられた面発光の光源からなり、試料台15の下側から培養容器10全体をバックライト照明する。第2照明部52は、LED等の光源と、位相リングやコンデンサレンズ等からなる照明光学系とを有して培養室2に設けられており、試料台15の上方から顕微観察系55の光軸に沿って培養容器10中の試料を照明する。第3照明部53は、それぞれ落射照明観察や蛍光観察に好適な波長の光を射出する複数のLEDや水銀等の光源と、各光源から射出された光を顕微観察系55の光軸に重畳させるビームスプリッタや蛍光フィルタ等からなる照明光学系とを有して、培養室2の下側に位置する下部フレーム1b内に配設されており、試料台15の下方から顕微観察系55の光軸に沿って培養容器10中の試料を照明する。 The first illumination unit 51 is composed of a surface-emitting light source provided on the lower frame 1b side, and backlight-illuminates the entire culture vessel 10 from the lower side of the sample stage 15. The second illumination unit 52 includes a light source such as an LED and an illumination optical system including a phase ring, a condenser lens, and the like. The second illumination unit 52 is provided in the culture chamber 2 and receives light from the microscopic observation system 55 from above the sample stage 15. The sample in the culture vessel 10 is illuminated along the axis. The third illumination unit 53 superimposes light emitted from each of the light sources, such as a plurality of LEDs and mercury that emit light having a wavelength suitable for epi-illumination observation and fluorescence observation, on the optical axis of the microscopic observation system 55. An illumination optical system composed of a beam splitter, a fluorescent filter, and the like to be disposed in the lower frame 1b located below the culture chamber 2, and the light of the microscopic observation system 55 from below the sample stage 15 The sample in the culture vessel 10 is illuminated along the axis.
 マクロ観察系54は、観察光学系54aと、この観察光学系54aにより結像された試料の像を撮影するCCDカメラ等の撮像装置54cとを有し、第1照明部51の上方に位置して培養室2内に設けられている。マクロ観察系54は、第1照明部51によりバックライト照明された培養容器10の上方からの全体観察画像(マクロ画像)を撮影する。 The macro observation system 54 includes an observation optical system 54 a and an imaging device 54 c such as a CCD camera that captures an image of the sample imaged by the observation optical system 54 a and is positioned above the first illumination unit 51. Provided in the culture chamber 2. The macro observation system 54 captures an entire observation image (macro image) from above the culture vessel 10 that is backlit by the first illumination unit 51.
 顕微観察系55は、対物レンズや中間変倍レンズ、蛍光フィルタ等からなる観察光学系55aと、観察光学系55aにより結像された試料の像を撮影する冷却CCDカメラ等の撮像装置55cとを有し、下部フレーム1bの内部に配設されている。上記の第2照明部52と顕微観察系55とにより位相差観察用の顕微鏡が構成される。対物レンズ及び中間変倍レンズは、それぞれ複数設けられるとともに、詳細図示を省略するレボルバやスライダなどの変位機構を用いて複数倍率に設定可能に構成されており、初期選択のレンズ設定に応じて、本実施形態では少なくとも低倍観察用(例えば2倍観察用)と高倍観察用(例えば10倍観察用)との2種類の倍率の間で変倍可能なように切り換えられる。顕微観察系55は、第2照明部52により照明された試料の透過光による位相差画像や、第3照明部53により照明されて試料が発する蛍光による蛍光画像など、培養容器10内の試料を顕微鏡観察した顕微観察画像(ミクロ画像)を撮影する。 The microscopic observation system 55 includes an observation optical system 55a composed of an objective lens, an intermediate zoom lens, a fluorescent filter, and the like, and an imaging device 55c such as a cooled CCD camera that takes an image of a sample imaged by the observation optical system 55a. And disposed inside the lower frame 1b. The second illumination unit 52 and the microscopic observation system 55 constitute a phase difference observation microscope. A plurality of objective lenses and intermediate zoom lenses are provided, and are configured to be set to a plurality of magnifications using a displacement mechanism such as a revolver or a slider (not shown in detail). In the present embodiment, at least two magnifications for low-magnification observation (for example, for double-magnification observation) and high-magnification observation (for example for ten-times observation) are switched so as to be variable. The microscopic observation system 55 displays the sample in the culture vessel 10 such as a phase difference image by the transmitted light of the sample illuminated by the second illumination unit 52 and a fluorescence image by the fluorescence emitted from the sample illuminated by the third illumination unit 53. A microscopic observation image (micro image) is taken.
 画像処理装置100は、マクロ観察系54の撮像装置54c及び顕微観察系55の撮像装置55cから入力された信号をA/D変換するとともに、各種の画像処理を施して全体観察画像または顕微観察画像の画像データを生成する。また、画像処理装置100は、これらの観察画像(全体観察画像及び顕微観察画像)の画像データに対して画像解析を施し、画像中に存在するオブジェクトの特徴量算出や、各々の特徴量に応じたスコア算出、総合スコアに基づく受精卵の決定等の画像処理を行う。画像処理装置100は、具体的には、次述する制御ユニット6のROMに記憶された画像処理プログラムが実行されることにより構築される。なお、この画像処理装置100については、後に詳述する。 The image processing apparatus 100 performs A / D conversion on signals input from the imaging device 54c of the macro observation system 54 and the imaging device 55c of the microscopic observation system 55, and performs various image processing to perform an entire observation image or a microscopic observation image. Image data is generated. Further, the image processing apparatus 100 performs image analysis on the image data of these observation images (entire observation image and microscopic observation image), calculates the feature amount of an object present in the image, and responds to each feature amount. Image processing such as calculating the score and determining the fertilized egg based on the total score. Specifically, the image processing apparatus 100 is constructed by executing an image processing program stored in the ROM of the control unit 6 described below. The image processing apparatus 100 will be described in detail later.
 制御ユニット6は、処理を実行するCPU61、培養観察システムBSの制御プログラムや制御データ等が設定記憶されたROM62、観察条件や画像データ等を一時記憶するRAM63などを有し、培養観察システムBSの作動を制御する。そのため、図3に示すように、培養室2、搬送装置4、観察ユニット5、操作盤7の各構成機器が制御ユニット6に接続されている。RAM63には、観察プログラムに応じた培養室2の環境条件や、観察スケジュール、観察ユニット5における観察種別や観察位置、観察倍率等が設定され記憶される。また、RAM63には、観察ユニット5により撮影された画像データを記録する画像データ記憶領域が設けられ、培養容器10のコード番号や撮影日時等を含むインデックス・データと画像データとが対応付けて記録される。 The control unit 6 includes a CPU 61 that executes processing, a ROM 62 that stores and stores control programs and control data of the culture observation system BS, a RAM 63 that temporarily stores observation conditions and image data, and the like. Control operation. Therefore, as shown in FIG. 3, the constituent devices of the culture chamber 2, the transport device 4, the observation unit 5, and the operation panel 7 are connected to the control unit 6. In the RAM 63, the environmental conditions of the culture chamber 2 according to the observation program, the observation schedule, the observation type and observation position in the observation unit 5, the observation magnification, and the like are set and stored. Further, the RAM 63 is provided with an image data storage area for recording image data photographed by the observation unit 5, and index data including the code number of the culture vessel 10 and photographing date / time are associated with the image data and recorded. Is done.
 操作盤7には、キーボードやスイッチ等の入出力機器が設けられた操作パネル71、操作画面や画像データ等を表示する表示パネル72が設けられ、操作パネル71において観察プログラムの設定や条件選択、動作指令等の入力が行われる。通信部65は有線または無線の通信規格に準拠して構成されており、この通信部65に外部接続されるコンピュータ等との間でデータの送受信が可能になっている。 The operation panel 7 is provided with an operation panel 71 provided with input / output devices such as a keyboard and a switch, and a display panel 72 for displaying an operation screen, image data, and the like. An operation command or the like is input. The communication unit 65 is configured in accordance with a wired or wireless communication standard, and data can be transmitted to and received from a computer or the like externally connected to the communication unit 65.
 このように概要構成される培養観察システムBSでは、操作盤7において設定された観察プログラムの設定条件に従い、CPU61がROM62に記憶された制御プログラムに基づいて各部の作動を制御するとともに、培養容器10内の試料の撮影を自動的に実行する。すなわち、操作パネル71に対するパネル操作(または通信部65を介したリモート操作)によって観察プログラムがスタートされると、CPU61が、RAM63に記憶された環境条件の各条件値を読み込むとともに、環境センサ25から入力される培養室2の環境状態を検出し、条件値と実測値との差異に応じて温度調整装置21、加湿器22、ガス供給装置23、循環ファン24等を作動させて、培養室2の温度や湿度、二酸化炭素濃度などの培養環境についてフィードバック制御が行われる。 In the culture observation system BS schematically configured as described above, the CPU 61 controls the operation of each part based on the control program stored in the ROM 62 according to the setting conditions of the observation program set on the operation panel 7, and the culture vessel 10 The sample inside is automatically captured. That is, when the observation program is started by a panel operation on the operation panel 71 (or a remote operation via the communication unit 65), the CPU 61 reads each condition value of the environmental conditions stored in the RAM 63, and from the environment sensor 25. The environmental state of the culture chamber 2 to be input is detected, and the temperature adjustment device 21, the humidifier 22, the gas supply device 23, the circulation fan 24, etc. are operated according to the difference between the condition value and the actual measurement value. Feedback control is performed on the culture environment such as temperature, humidity, and carbon dioxide concentration.
 また、CPU61は、RAM63に記憶された観察条件を読み込む、観察スケジュールに基づいて搬送ユニット4のX,Y,Zステージ41,42,43を作動させてストッカー3から観察対象の培養容器10を観察ユニット5の試料台15に搬送して、観察ユニット5による観察を開始させる。例えば、観察プログラムにおいて設定された観察がマクロ観察である場合には、搬送ユニット4によりストッカー3から搬送してきた培養容器10をマクロ観察系54の光軸上に位置決めして試料台15に載置し、第1照明部51の光源を点灯させて、バックライト照明された培養容器10の上方から撮像装置54cにより全体観察像を撮影する。撮像装置54cから制御ユニット6に入力された信号は、画像処理装置100により処理されて全体観察画像が生成され、その画像データが撮影日時等のインデックス・データなどとともにRAM63の画像データ記憶領域に記憶される。 Further, the CPU 61 reads the observation conditions stored in the RAM 63 and operates the X, Y, Z stages 41, 42, 43 of the transport unit 4 based on the observation schedule to observe the culture vessel 10 to be observed from the stocker 3. The sample is transported to the sample stage 15 of the unit 5 and observation by the observation unit 5 is started. For example, when the observation set in the observation program is macro observation, the culture vessel 10 transported from the stocker 3 by the transport unit 4 is positioned on the optical axis of the macro observation system 54 and placed on the sample stage 15. Then, the light source of the first illumination unit 51 is turned on, and the entire observation image is taken by the imaging device 54c from above the culture vessel 10 that is backlit. The signal input from the imaging device 54c to the control unit 6 is processed by the image processing device 100 to generate a whole observation image, and the image data is stored in the image data storage area of the RAM 63 together with index data such as the shooting date and time. Is done.
 また、観察プログラムにおいて設定された観察が、培養容器10内の特定位置の試料のミクロ観察である場合には、搬送ユニット4により搬送してきた培養容器10内の特定位置を顕微観察系55の光軸上に位置決めして試料台15に載置し、第2照明部52または第3照明部53の光源を点灯させて、透過照明、落射照明、蛍光による顕微観察像を撮像装置55cに撮影させる。撮像装置55cにより撮影されて制御ユニット6に入力された信号は、画像処理装置100により処理されて顕微観察画像(位相差画像、蛍光画像等)が生成され、その画像データが撮影日時等のインデックス・データなどとともにRAM63の画像データ記憶領域に記憶される。 In addition, when the observation set in the observation program is micro observation of the sample at a specific position in the culture vessel 10, the specific position in the culture vessel 10 that has been transported by the transport unit 4 is indicated by the light of the microscopic observation system 55. Position on the axis and place on the sample stage 15, turn on the light source of the second illumination unit 52 or the third illumination unit 53, and cause the imaging device 55c to take a microscopic observation image by transmitted illumination, epi-illumination, and fluorescence. . A signal photographed by the imaging device 55c and inputted to the control unit 6 is processed by the image processing device 100 to generate a microscopic observation image (phase difference image, fluorescent image, etc.), and the image data is an index such as a photographing date / time. Stored in the image data storage area of the RAM 63 together with the data.
 CPU61は、上記のような全体観察像の撮影や顕微観察像の撮影を、観察プログラムに設定された観察スケジュールに基づいて順次実行する。RAM63に記憶された画像データは、操作パネル71から入力される画像表示指令に応じてRAM63から読み出され、例えば指定時刻の全体観察画像や顕微観察画像、画像解析の解析結果などが表示パネル72に表示される。 The CPU 61 sequentially executes the above-described whole observation image photographing and microscopic observation image photographing based on the observation schedule set in the observation program. The image data stored in the RAM 63 is read from the RAM 63 in response to an image display command input from the operation panel 71. For example, an entire observation image, a microscopic observation image at a specified time, an analysis result of image analysis, and the like are displayed on the display panel 72. Is displayed.
 以上のように構成される培養観察システムBSにおいて、画像処理装置100は、培養容器10内における受精卵の生育状態等の観察を実行するため、観察対象である受精卵と、それ以外のゴミや気泡等の異物(非対称物)とを判別する機能を備えている。すなわち、培養容器10内の培地ドロップDには、受精卵の他にもゴミや気泡等の他のオブジェクトが混入しており、それらは受精卵と似た外観を持つことがあり観察において混同しやすいため、画像処理装置100では、受精卵とそれ以外の異物とを判別して観察対象たる受精卵を自動で認識する機能を有している。 In the culture observation system BS configured as described above, the image processing apparatus 100 performs observation of the growth state and the like of the fertilized egg in the culture container 10, so that the fertilized egg to be observed and other garbage or the like It has a function of discriminating foreign substances (asymmetrical substances) such as bubbles. That is, the medium drop D in the culture container 10 is mixed with other objects such as dust and bubbles in addition to the fertilized egg, which may have a similar appearance to the fertilized egg and are confused in observation. Since it is easy, the image processing apparatus 100 has a function of automatically discriminating a fertilized egg to be observed by discriminating a fertilized egg from other foreign substances.
 ここで、培地ドロップD内の混入物は、ゴミ(培地の残りかす)、オイル粒(ディッシュ10a内に充填されるミネラルオイルOが培地に入り込んだもの)、及び気泡(ガス)に大別される。このとき、図5にも示すように、受精卵、ゴミ、オイル粒、及び気泡の各々は次のような特徴を持っている。 Here, the contaminants in the medium drop D are roughly classified into garbage (remaining residue of the medium), oil particles (the mineral oil O filled in the dish 10a enters the medium), and bubbles (gas). The At this time, as shown in FIG. 5, each of the fertilized egg, dust, oil particles, and bubbles has the following characteristics.
(受精卵の特徴)
 受精卵は、その輪郭部に膜(透明帯)が存在する、外形が球形状を有している、輪郭内部に卵細胞が存在している、という特徴を持つ。
(Characteristics of fertilized eggs)
A fertilized egg has a feature that a contour (a zona pellucida) is present in the contour, the outer shape is spherical, and an egg cell is present in the contour.
(ゴミの特徴)
 ゴミは、その輪郭部に透明帯が存在しない、位相差画像では輪郭部にハロが現れる、輪郭内部の構造は不定である、という特徴を持つ。
(Characteristics of garbage)
The dust has the characteristics that there is no transparent band in the outline, halo appears in the outline in the phase difference image, and the structure inside the outline is indefinite.
(オイル粒、気泡の特徴)
 オイル粒および気泡は、位相差画像では輪郭部が暗く内部は明るい、外形が球形状や楕円形状である、輪郭内部の構造が少ない、という特徴を持つ。
(Features of oil particles and bubbles)
In the phase difference image, the oil particles and bubbles have the characteristics that the contour portion is dark and the inside is bright, the outer shape is spherical or elliptical, and the structure inside the contour is small.
 これらの特徴を簡単にまとめると、受精卵と異物との属性の差異として、受精卵は異物よりも円形度が高い、受精卵は輪郭部に透明帯を持っている、受精卵には内部構造(卵細胞)が必ず存在する、という特徴がある。 To summarize these characteristics, the fertilized egg has a higher degree of circularity than the foreign object, the fertilized egg has a zona pellucida in the contour, the internal structure of the fertilized egg (Egg cells) are always present.
 そこで、取得した観察画像から観察対象である受精卵を認識するために、画像特徴量として、3つの特徴量(すなわち以下に詳述する、I.輪郭の円形度、II.輪郭の外側と内側の輝度差、III.輪郭内部のテクスチャ特徴量)を受精卵と異物との判別に利用する。 Therefore, in order to recognize a fertilized egg as an observation target from the acquired observation image, three feature amounts (that is, I. Circularity of contour, II. Outside and inside of contour, which will be described in detail below) are used as image feature amounts. Luminance difference, III. Texture feature amount in the contour) is used for discrimination between fertilized eggs and foreign objects.
 画像処理装置100による受精卵観察の画像処理方法は、観察ユニット5の観察視野内に位置する物体を撮像装置により撮影した画像を取得し、これらの画像に写し込まれた物体(オブジェクト)の像を抽出して上記3つの特徴量を算出し、この特徴量に基づき得点付け(スコア算出)を行った結果、これら3つの特徴量について最も高いスコアを示すものが受精卵であると決定するように構成される。それでは、以下にこの画像処理方法について基本的な概念から説明する。なお、以降の説明では、第2照明部52及び顕微観察系55等によって構成される位相差顕微鏡によって撮影される位相差画像(顕微観察画像)に基づいて受精卵の観察を行う場合を例示する。 The image processing method for observing a fertilized egg by the image processing apparatus 100 acquires images obtained by photographing an object located in the observation field of view of the observation unit 5 with an imaging device, and images of the objects (objects) imprinted in these images. As a result of calculating the above three feature quantities and scoring (score calculation) based on these feature quantities, it is determined that the fertilized egg is the one that has the highest score among these three feature quantities. Configured. In the following, this image processing method will be described from the basic concept. In the following description, a case where a fertilized egg is observed based on a phase contrast image (microscopic observation image) photographed by a phase contrast microscope configured by the second illumination unit 52, the microscopic observation system 55, and the like will be exemplified. .
(オブジェクトの抽出)
 受精卵の識別処理に先立って、撮像装置55cにより撮影された観察画像(位相差画像)に対して、LevelSet法などの輪郭抽出手法を適用して画像内に含まれる物体(受精卵及びゴミ等の異物の領域、オブジェクトとも称する)の最外輪郭を抽出し、その輪郭に囲まれた閉じた領域をオブジェクトとして抽出する。図6は輪郭抽出処理の様子を示す図であり、撮像装置55cにより撮影され取得された観察画像(A)に対し、(B)に示す2値画像のように複数のオブジェクトを抽出する。
(Extract objects)
Prior to discrimination processing of fertilized eggs, an object (fertilized egg, dust, etc.) included in the image by applying a contour extraction method such as the LevelSet method to the observation image (phase difference image) taken by the imaging device 55c. The outermost contour of the foreign object (also referred to as object) is extracted, and the closed region surrounded by the contour is extracted as the object. FIG. 6 is a diagram showing a state of the contour extraction process. A plurality of objects are extracted from the observation image (A) photographed and acquired by the imaging device 55c as in the binary image shown in (B).
(オブジェクトのラベリング)
 こうしてセグメント化された各オブジェクト(の2値画像)に対して、固有のラベルを付与するラベリングを施す。ラベリング処理では、ラベル付けとともにオブジェクト(抽出した輪郭に囲まれた領域)の面積が算出される。そこで、観察画像に写し込まれる受精卵(例えば、人間の受精卵の場合には直径約100μm)の像の大きさは画像取得条件(観察倍率等)によりほぼ決まっていることから、判別の対象となり得るオブジェクトの面積の上限値と下限値とを予め設定しておき、この設定範囲から外れるオブジェクトについてはラベル付けの対象(受精卵の判別候補)から除外する。
(Object labeling)
Each segmented object (binary image) is labeled to give a unique label. In the labeling process, the area of the object (area surrounded by the extracted contour) is calculated together with labeling. Therefore, the size of an image of a fertilized egg (for example, a diameter of about 100 μm in the case of a human fertilized egg) is almost determined by the image acquisition conditions (observation magnification, etc.), and is therefore subject to discrimination. An upper limit value and a lower limit value of a possible area of the object are set in advance, and an object outside the set range is excluded from a labeling target (fertilized egg discrimination candidate).
(オブジェクトの特徴量抽出)
 ラベリングを施した各オブジェクトについて、以下に詳述する、I.輪郭の円形度、II.輪郭の外側と内側の輝度差、III.輪郭内部のテクスチャ特徴量、の3つの特徴量をそれぞれ算出する。
(Extraction of object features)
Each object that has been labeled will be described in detail below. Contour circularity, II. Brightness difference between outside and inside of the contour, III. The three feature amounts of the texture feature amount inside the contour are calculated.
 1.輪郭の円形度(特徴量1)
 観察対象の受精卵は球形状(断面が真円に近い)であることを利用して、各オブジェクトの円形度を特徴量1として用いる。オブジェクトの円形度とはオブジェクトの円形の度合いを判定するための尺度であり、例えば、画像平面内における各々のオブジェクトの重心を決定するとともに各々のオブジェクトの輪郭を示すエッジを検出した後、オブジェクトごとに自身の重心から輪郭までの最短距離と最長距離とを算出し、次式(α)より求められる。
 輪郭の円形度
   =(重心から輪郭までの最短距離)/(重心から輪郭までの最長距離) …(α)
これにより求められる円形度は0~1の範囲内の数値として算出され、重心から輪郭までの距離が均等な円形となるほど1に近い数値を示す。つまり、1に近い円形度を示すオブジェクトほど受精卵としてのスコアが高いことになる(受精卵である可能性が高い)。なお、輪郭の円形の度合いを示す別の指標としては、形状の複雑度(輪郭の周囲長2/面積)や、二次モーメントを利用した離心率などが例示される。
1. Contour circularity (feature 1)
Utilizing the fact that the fertilized egg to be observed has a spherical shape (the cross section is close to a perfect circle), the circularity of each object is used as the feature amount 1. The degree of circularity of an object is a scale for determining the degree of circularity of an object. For example, after determining the center of gravity of each object in the image plane and detecting the edge indicating the outline of each object, Then, the shortest distance and the longest distance from the center of gravity to the contour are calculated and obtained from the following equation (α).
Contour circularity = (shortest distance from centroid to contour) / (longest distance from centroid to contour)… (α)
The circularity obtained in this way is calculated as a numerical value within the range of 0 to 1, and indicates a numerical value closer to 1 as the distance from the center of gravity to the contour becomes a uniform circular shape. That is, an object having a circularity close to 1 has a higher score as a fertilized egg (highly likely to be a fertilized egg). In addition, as another index indicating the degree of circularity of the contour, the complexity of the shape (contour circumference 2 / area), the eccentricity using the second moment, and the like are exemplified.
 2.輪郭の外側と内側の輝度差(特徴量2)
 観察対象の受精卵の輪郭部には透明帯と称される透明な薄膜が存在する。図7に示す顕微観察系55による観察画像では、この透明帯の画像は培地ドロップD等の背景に近い輝度値を持つため、輪郭の外側と内側との輝度値の差は小さい。それに対して、オイル粒と気泡の輪郭内側は外側の背景に対して暗く、位相差画像ではゴミの輪郭内側はハロによって明るくなる傾向があるため、輪郭の外側と内側との輝度値の差は大きい。そこで、透明帯の特徴を利用し、特徴量2として、オブジェクトにおける輪郭外側の輪帯部(外輪帯)と輪郭内側の輪帯部(内輪帯)との平均輝度値(例えば8ビット階調の場合に0~255)の差を用いて、輝度差を次式(β)により求める。
 輪郭の外側と内側の輝度差
   =(255-|内輪帯平均輝度値-外輪帯平均輝度値|)/255 …(β)
2. Brightness difference between outside and inside contour (feature value 2)
A transparent thin film called a zona pellucida exists in the outline of the fertilized egg to be observed. In the observation image by the microscopic observation system 55 shown in FIG. 7, since the image of the zona pellucida has a luminance value close to the background such as the medium drop D, the difference in luminance value between the outside and inside of the contour is small. On the other hand, the inside of the outline of oil particles and bubbles is darker than the background outside, and in the phase contrast image, the inside of the outline of dust tends to be brightened by halo, so the difference in brightness value between the outside and inside of the outline is large. Therefore, using the characteristics of the zona pellucida, as the feature amount 2, an average luminance value (for example, 8-bit gradation) between the outer ring zone (outer ring zone) and the inner ring zone (inner ring zone) of the contour in the object is used. In this case, the difference in brightness is obtained by the following equation (β) using the difference of 0 to 255).
Brightness difference between outside and inside contour = (255− | inner annular average luminance value−outer annular average luminance value |) / 255 (β)
 図8は、外輪帯及び内輪帯の平均輝度値を算出するときのマスク生成処理の状況を例示した図であり、これらの輝度値算出の際には、上述の処理で得られた画像(ラベリングが施された2値画像)を利用する。まず、各オブジェクトの2値画像(ラベル画像)に対して膨張及び収縮処理を施して、この膨張及び収縮した領域を示す2種類のマスク画像(膨張マスクM1、収縮マスクM2)をオブジェクトごとに用意する。次いでオブジェクトごとに、その重心を一致させた状態で2つのマスク画像M1,M2と元となる原ラベル画像との領域の差分を演算し、これらの略リング状の差分画像を、オブジェクトの輪郭に対して外側及び内側の領域を示す外側差分マスクM3及び内側差分マスクM4として生成する。この2つの差分マスクM3,M4の領域に対応する観察画像(撮像装置55cにより撮像された位相差画像)の領域内の輝度値の平均をそれぞれ算出し、これを外輪帯平均輝度値及び内輪帯平均輝度値とする。これらを上記式(β)に代入することで、特徴量2として、輪郭の外側と内側の輝度差が求められる。内輪帯平均輝度値と外側帯平均輝度値との間の差が小さいオブジェクトほど1に近い数値が得られ、受精卵としてのスコアが高いといえる(受精卵である可能性が高い)。 FIG. 8 is a diagram illustrating the state of mask generation processing when calculating the average luminance value of the outer annular zone and the inner annular zone. When calculating these luminance values, the images (labeling) obtained by the above-described processing are illustrated. A binary image) is used. First, the binary image (label image) of each object is subjected to expansion and contraction processing, and two types of mask images (expansion mask M1 and contraction mask M2) showing the expanded and contracted regions are prepared for each object. To do. Next, for each object, the difference between the areas of the two mask images M1 and M2 and the original original label image is calculated in a state where the centers of gravity coincide with each other, and these substantially ring-shaped difference images are used as the outline of the object. On the other hand, they are generated as an outer difference mask M3 and an inner difference mask M4 indicating outer and inner regions. The average of the luminance values in the region of the observation image (the phase difference image captured by the imaging device 55c) corresponding to the regions of the two difference masks M3 and M4 is calculated, and these are calculated as the outer annular average luminance value and the inner annular zone. The average luminance value. By substituting these into the above equation (β), the brightness difference between the outside and inside of the contour is obtained as the feature amount 2. An object with a smaller difference between the inner ring zone average brightness value and the outer zone average brightness value has a numerical value closer to 1, and it can be said that the score as a fertilized egg is higher (highly likely to be a fertilized egg).
 3.輪郭内部のテクスチャ特徴量(特徴量3)
 観察対象の受精卵の内部には卵細胞が収まっており、画像的にはテクスチャ(内部テクスチャ)が存在すると考えられる。これに対して、オイル粒、気泡には内部構造が存在しない。よって、各オブジェクトの内部構造を検出することにより、受精卵と、オイル粒及び気泡との差別化を図ることができる。内部構造を検出するためのオブジェクトの領域については、図9に示すように、上記処理で生成した収縮マスクM2を利用して、観察画像内における自身の収縮マスクM2に対応した画像領域(以下、「収縮マスク領域」とも称する)を対象として扱う。オブジェクト内部のテクスチャ特徴量としては、収縮マスク領域内の画像全体におけるエッジ強度(階調変化の強度)の平均値を用いる(次式(γ)を参照)。エッジ強度については、各オブジェクトの収縮マスク領域の画像に対して、例えば3×3マトリクスや5×5マトリクスなどで示されるラプラシアンフィルタ等の微分フィルタを適用した畳み込み演算により画素単位で求める。
 輪郭内部のテクスチャ特徴量
    =収縮マスク領域内のエッジ強度の総和/収縮マスク領域内の画素数 …(γ)
ここでエッジ強度としては、輝度の階調変化の方向を考慮して正値となるエッジ強度のみを利用し、負値となるエッジ強度については無効なエッジ(エッジ強度=0)として取り扱う。また、エッジ強度の平均を求めるために、収縮マスク領域内に含まれる画素数を用いる。そして、このようにオブジェクトごとに収縮マスク領域内のエッジ強度を画素単位で算出して得られた平均値を、収縮マスク領域内の画素数で特徴量3であるオブジェクト内部のテクスチャ特徴量が求まる。
3. Texture feature amount inside contour (feature amount 3)
Egg cells are contained inside the fertilized egg to be observed, and it is considered that a texture (internal texture) exists on the image. On the other hand, oil grains and bubbles do not have an internal structure. Therefore, by detecting the internal structure of each object, it is possible to differentiate a fertilized egg from oil particles and bubbles. As for the region of the object for detecting the internal structure, as shown in FIG. 9, using the contraction mask M2 generated by the above processing, an image region corresponding to its contraction mask M2 in the observation image (hereinafter, referred to as “image region”). Also referred to as “shrink mask area”. As the texture feature amount inside the object, an average value of edge strength (gradation change strength) in the entire image in the contraction mask region is used (see the following equation (γ)). The edge strength is obtained on a pixel basis by a convolution operation in which a differential filter such as a Laplacian filter represented by, for example, a 3 × 3 matrix or a 5 × 5 matrix is applied to the image of the contraction mask region of each object.
Texture feature amount inside contour = total edge intensity in shrink mask area / number of pixels in shrink mask area (γ)
Here, as the edge strength, only the edge strength having a positive value is used in consideration of the direction of luminance gradation change, and the edge strength having a negative value is treated as an invalid edge (edge strength = 0). In addition, the number of pixels included in the contraction mask region is used to obtain the average edge intensity. Then, an average value obtained by calculating the edge strength in the contraction mask area for each object in units of pixels in this way is obtained as the texture feature quantity inside the object, which is the feature quantity 3 by the number of pixels in the contraction mask area. .
 以上の3つの特徴量はラベリングされたオブジェクトごとに与えられており、これを基に受精卵を決定する The above three feature values are given for each labeled object, and the fertilized egg is determined based on this.
(受精卵の判別)
 まず、(I)明らかに受精卵とは異なる特徴を持つオブジェクトをしきい値にてリジェクトする。次に、(II)各特徴量の単位やスケーリングが相違することを考慮して3つの特徴量を正規化し、正規化して得られた各スコア1,2,3の総和を総合スコアとして各オブジェクトについて得点付けを行う。1つの培地ドロップDに対して注入される受精卵は1つであることから、最も高い総合スコアを持つオブジェクトが受精卵であると決定する。この具体的な手法について以下に例示する。
(Determination of fertilized eggs)
First, (I) an object having clearly different characteristics from a fertilized egg is rejected with a threshold value. Next, (II) normalizing the three feature values in consideration of differences in units and scaling of each feature value, and summing the scores 1, 2, and 3 obtained by normalization, the total score is used for each object. Scoring for. Since one fertilized egg is injected for one medium drop D, it is determined that the object having the highest overall score is a fertilized egg. This specific method is exemplified below.
(I:しきい値によるリジェクト)
 受精卵ではないオブジェクトが持つ特徴量には次のような傾向がある。オイル粒及び気泡については、特徴量2(内外輝度差)と特徴量3(内部テクスチャ)とが小さいという傾向。ゴミについては、特徴量1(円形度)と特徴量2(内外輝度差)とが小さいという傾向。そこで、予めしきい値を定めておき、明らかに外れた特徴量を持つオブジェクトについては、このしきい値によってリジェクトしておき、得点付け(スコア)による誤検出を防止する。このとき、特徴量3の輪郭内部のテクスチャ特徴量に関しては、その値(エッジ強度の平均値)そのものを用いるのではなく、ラベリングされた全オブジェクトにおけるエッジ強度平均値の最大値に対する割合(エッジ強度平均値/エッジ強度平均値の最大値)を利用する。これは、特徴量3については、画像のコントラストによってエッジ強度が極端に大きくなったり小さくなったりと変化するため、一定のしきい値を設けること自体に意味がなくなるからであり、コントラストの影響を取り除くため、全オブジェクトの画像間で正規化をする。また、内部テクスチャが存在しない場合(例えば、気泡のみが写し込まれた場合など)には、本来エッジ強度が全体的に小さく現れるところ、正規化を行うことにより全てのオブジェクトについて特徴量3が大きくなり過ぎる虞があるため、予め許容値を定めておき、エッジ強度平均値の最大値がこの許容値を下回ったときには、特徴量3については全てをリジェクトする。
(I: Reject by threshold)
Features of objects that are not fertilized eggs tend to be as follows. For oil particles and bubbles, the feature amount 2 (internal / external luminance difference) and the feature amount 3 (internal texture) tend to be small. As for dust, feature amount 1 (circularity) and feature amount 2 (internal / external luminance difference) tend to be small. Therefore, a threshold value is set in advance, and an object having a feature quantity that is clearly deviated is rejected by this threshold value to prevent erroneous detection due to scoring (score). At this time, with respect to the texture feature amount inside the outline of the feature amount 3, the value (average value of edge strength) itself is not used, but the ratio (edge strength) to the maximum value of the average value of edge strength in all labeled objects. Average value / maximum value of edge strength average value) is used. This is because, for the feature amount 3, the edge strength changes extremely depending on the contrast of the image, so that it is meaningless to provide a certain threshold value. Normalize between images of all objects to remove. Further, when there is no internal texture (for example, when only bubbles are captured), the edge strength originally appears to be small as a whole, but by performing normalization, the feature amount 3 is large for all objects. Since there is a risk of becoming too much, a permissible value is set in advance, and when the maximum value of the edge strength average value falls below this permissible value, all of the feature amount 3 is rejected.
(II:特徴量のスケール正規化)
 特徴量1~3に対してスケールの正規化を行って、各特徴量を共通のスケール(例えば0~1のスケール)に変換する。具体的には、各特徴量について以下のような上下限を設定し、
 特徴量1(円形度)    :下限=特徴量1のしきい値、上限=特徴量1の最大値
 特徴量2(内外輝度差)  :下限=特徴量2のしきい値、上限=特徴量2の最大値
 特徴量3(内部テクスチャ):下限=0、上限=特徴量3の最大値
その上下限内の範囲をフルスケールとして各特徴量の正規化を行う。なお、特徴量3については、観察画像のコントラストに依存するという特質を有するのでしきい値を設けるのが難しいため、その下限値を0とするとともに、上限値については上記の許容値を下回る際には0として、特徴量3のスコア付けは行わない。
(II: Feature scale normalization)
Scale normalization is performed on the feature quantities 1 to 3, and each feature quantity is converted to a common scale (for example, a scale of 0 to 1). Specifically, the following upper and lower limits are set for each feature amount,
Feature amount 1 (circularity): lower limit = threshold of feature amount 1, upper limit = maximum value of feature amount 1 Feature amount 2 (internal / external luminance difference): lower limit = threshold value of feature amount 2, upper limit = feature amount 2 Feature value 3 (internal texture): lower limit = 0, upper limit = maximum value of feature value 3 The range within the upper and lower limits thereof is normalized as a full scale. Since the feature amount 3 has a characteristic that it depends on the contrast of the observation image, it is difficult to provide a threshold value. Therefore, the lower limit value is set to 0, and the upper limit value is less than the above allowable value. Is set to 0, and scoring of the feature amount 3 is not performed.
 そして、正規化されて共通のスケールに変換された各特徴量をスコア1,2,3とし、それらの総和となる総合スコアを算出する。
 総合スコア=スコア1+スコア2+スコア3
その結果、総合スコアが培地ドロップD内で最大となるオブジェクトを受精卵と決定する。
Then, the feature quantities normalized and converted to a common scale are set as scores 1, 2, and 3, and a total score that is the sum of them is calculated.
Overall score = Score 1 + Score 2 + Score 3
As a result, the object having the maximum total score in the medium drop D is determined as a fertilized egg.
 以上説明したような受精卵の判別手法によれば、観察画像内に観察対象となる受精卵以外の他の複数のオブジェクトが存在する場合でも、受精卵と他のオブジェクトとを区別するための複数の特徴量を用いて、それを基に受精卵らしさの度合いを示すスコアを算出し、このスコアの高さにより受精卵と異物とを明確に分離して受精卵を認識することが可能になる。 According to the method for discriminating a fertilized egg as described above, even when there are a plurality of objects other than the fertilized egg to be observed in the observation image, a plurality of pieces for distinguishing the fertilized egg from other objects Using this feature amount, a score indicating the degree of fertility is calculated based on the feature amount, and the fertilized egg can be recognized clearly by separating the fertilized egg from the foreign substance based on the height of this score. .
 なお、各スコアに基づく受精卵の決定手法については、各スコア1~3の単なる総和としての総合スコアによる順位付けの他にも、例えば、各スコア1~3に対する重み付けをそれぞれ変えた総合スコアを算出して受精卵を決定したり、各スコア1~3ごとに順位付けをして順位の総和の最も小さいオブジェクトを受精卵であると判断したり、スコア1,2で上位となったオブジェクトのうちでスコア3の最も高いものを受精卵であると判断する、などの手法が例示される。また、総合スコアがしきい値を超えたものを受精卵として認識する構成であってもよい。 Regarding the method of determining a fertilized egg based on each score, in addition to ranking based on the total score as a simple sum of the scores 1 to 3, for example, a total score obtained by changing the weights for the scores 1 to 3 respectively. Calculate and determine the fertilized egg, rank each score 1 to 3 and judge that the object with the lowest total rank is a fertilized egg, Among them, a method of determining that the highest score is 3 is a fertilized egg. Moreover, the structure which recognizes that a total score exceeded the threshold value as a fertilized egg may be sufficient.
(アプリケーション)
 次に、培養観察システムBSの画像処理装置100において実行される画像解析の具体的なアプリケーションについて図1及び図10を併せて参照しながら説明する。ここで、図1は受精卵判別の画像処理プログラムGPにおける処理の概要を示すフローチャート、図10は受精卵判別の画像処理を実行する画像処理装置100の概要構成を示すブロック図である。
(application)
Next, a specific application of image analysis executed in the image processing apparatus 100 of the culture observation system BS will be described with reference to FIGS. Here, FIG. 1 is a flowchart showing an outline of processing in the image processing program GP for fertilized egg discrimination, and FIG. 10 is a block diagram showing a schematic configuration of an image processing apparatus 100 that executes image processing for fertilized egg discrimination.
 画像処理装置100は、撮像装置55cにより観察対象の受精卵が撮影された観察画像を取得して記憶する画像記憶部110と、観察画像を解析して観察画像に写し込まれたオブジェクトが受精卵であるか否かを判断する画像解析部120と、画像解析部120によりオブジェクトごとに算出された3つの特徴量を各オブジェクトに付与されるラベルと対応付けて記憶する特徴量記憶部130と、画像解析部120により解析された判断結果を外部に出力する出力部140とを備え、画像解析部120により判断されたオブジェクトが受精卵であるか否かの識別結果を、例えば表示パネル72に出力して表示させるように構成される。画像処理装置100は、ROM62に予め設定記憶された画像処理プログラムGPがCPU61に読み込まれ、CPU61によって画像処理プログラムGPに基づく処理が順次実行されることによって構成される。 The image processing apparatus 100 includes an image storage unit 110 that acquires and stores an observation image in which a fertilized egg to be observed is captured by the imaging device 55c, and an object that analyzes the observation image and is imprinted on the observation image. An image analysis unit 120 that determines whether or not the image analysis unit 120, a feature amount storage unit 130 that stores the three feature amounts calculated for each object by the image analysis unit 120 in association with labels assigned to the objects, An output unit 140 that outputs the determination result analyzed by the image analysis unit 120 to the outside, and outputs, for example, to the display panel 72 the identification result as to whether or not the object determined by the image analysis unit 120 is a fertilized egg Configured to be displayed. The image processing apparatus 100 is configured such that an image processing program GP preset and stored in the ROM 62 is read by the CPU 61 and processing based on the image processing program GP is sequentially executed by the CPU 61.
 既述したように、培養観察システムBSでは、観察プログラムにおいて設定された観察条件に従って、所定時間ごとに指定された培養容器10内の受精卵観察が行われる。具体的には、CPU61は、搬送ユニット4の各ステージを作動させてストッカー3から観察対象の培養容器10を観察ユニット5に搬送(本実施形態では顕微観察系55の光軸上に配置)し、第2照明部52を用いた顕微観察系55による観察画像(位相差画像)を撮像装置55cにより撮影させる。 As described above, in the culture observation system BS, the fertilized egg in the culture vessel 10 designated every predetermined time is observed according to the observation conditions set in the observation program. Specifically, the CPU 61 operates each stage of the transport unit 4 to transport the culture vessel 10 to be observed from the stocker 3 to the observation unit 5 (in this embodiment, it is arranged on the optical axis of the microscopic observation system 55). Then, an observation image (phase difference image) by the microscopic observation system 55 using the second illumination unit 52 is photographed by the imaging device 55c.
 画像処理装置100は、始めに撮像装置55cにより撮影された観察画像(位相差画像)をステップS1において取得し、この取得した観察画像を、培養容器10のコード番号や観察位置、観察時刻などのインデックス・データとともに画像記憶部110に保存する。 The image processing apparatus 100 first acquires an observation image (phase difference image) taken by the imaging device 55c in step S1, and acquires the acquired observation image such as the code number, the observation position, and the observation time of the culture vessel 10. It is stored in the image storage unit 110 together with the index data.
 ステップS2では、撮像装置55cから取得された観察画像(位相差画像)に対し、画像解析部120においてLevelSet法などの輪郭抽出処理が実行され、図6(B)に示したように、観察画像に含まれるオブジェクトが抽出される。 In step S2, contour extraction processing such as the LevelSet method is performed on the observation image (phase difference image) acquired from the imaging device 55c in the image analysis unit 120, and as shown in FIG. Objects included in are extracted.
 ステップS3では、輪郭が抽出された観察画像(2値画像)の各オブジェクトに対してラベリングが施される。ラベリング処理においては、受精卵の像の面積として適正な範囲(上下限値)が設定されており、輪郭が抽出されたオブジェクトの領域ごとに面積が算出され、この設定範囲を超える領域面積を持つオブジェクトについては受精卵の判別候補から除外し、この設定範囲内に収まる領域面積を持つオブジェクトのみについて受精卵の判別候補として固有のラベルをそれぞれ付与する。 In step S3, each object of the observation image (binary image) from which the contour is extracted is labeled. In the labeling process, an appropriate range (upper and lower limit values) is set as the area of the image of the fertilized egg, and the area is calculated for each object region from which the contour is extracted, and has a region area that exceeds this set range. Objects are excluded from the fertilized egg discrimination candidates, and only objects having a region area that falls within the set range are given unique labels as fertilized egg discrimination candidates.
 次いで、画像解析部120により受精卵の画像特徴量として、各オブジェクトについて、輪郭の円形度、輪郭の外側と内側の輝度差、及び輪郭内部のテクスチャ特徴量を算出する(ステップS4)。画像解析部120は、オブジェクトごとに付与した固有のラベルと、以下において算出される3つの特徴量とを対応付けて特徴量記憶部130に記録する。 Next, the image analysis unit 120 calculates the circularity of the contour, the brightness difference between the outside and inside of the contour, and the texture feature amount inside the contour as the image feature amount of the fertilized egg (step S4). The image analysis unit 120 records the unique label assigned to each object and the three feature amounts calculated below in the feature amount storage unit 130 in association with each other.
 輪郭の円形度(特徴量1)を求める処理フローF10において、画像解析部120はラベル画像からオブジェクトの重心を算出し、この重心から輪郭(エッジ)までの距離において、重心から輪郭までの最長距離に対する最短距離の割合を輪郭の円形度として算出する(ステップS11)。 In the processing flow F10 for calculating the circularity (feature value 1) of the contour, the image analysis unit 120 calculates the center of gravity of the object from the label image, and the longest distance from the center of gravity to the contour is the distance from the center of gravity to the contour (edge). The ratio of the shortest distance to is calculated as the circularity of the contour (step S11).
 輪郭の外側と内側の輝度差(特徴量2)を求める処理フローF20において、画像解析部120は、ラベル画像からオブジェクトごとに膨張マスクM1と収縮マスクM2との2つのマスク画像をそれぞれ生成した上で、この2つのマスク画像M1,M2と元のラベル画像との差分から差分マスクM3,M4をそれぞれ生成する。そして、撮像装置55cによって撮像された観察画像において外側差分マスクM3の領域と内側差分マスクM4の領域とに対応する領域内の輝度値の平均をそれぞれ求め、上記式(β)を用いて、輪郭の外側と内側の輝度差を算出する(ステップS21)。 In the processing flow F20 for obtaining the luminance difference (feature value 2) between the outer side and the inner side of the contour, the image analysis unit 120 generates two mask images of the expansion mask M1 and the contraction mask M2 for each object from the label image. Thus, difference masks M3 and M4 are respectively generated from the difference between the two mask images M1 and M2 and the original label image. Then, the average of the luminance values in the regions corresponding to the region of the outer differential mask M3 and the region of the inner differential mask M4 in the observation image captured by the imaging device 55c is obtained, and the contour is calculated using the above equation (β). The brightness difference between the outside and the inside is calculated (step S21).
 輪郭内部のテクスチャ特徴量(特徴量3)を求める処理フローF30において、ステップS21で生成した収縮マスクM2を用い、撮像装置55cによって撮像された観察画像について微分フィルタを適用させて、収縮マスクM2に対応した領域(収縮マスク領域)内のエッジ強度の平均を輪郭内部のテクスチャ特徴量として算出する(ステップS31)。 In the processing flow F30 for obtaining the texture feature amount (feature amount 3) inside the contour, the contraction mask M2 generated in step S21 is used, and a differential filter is applied to the observation image captured by the imaging device 55c, so that the contraction mask M2 is applied. The average edge strength in the corresponding region (shrink mask region) is calculated as the texture feature amount inside the contour (step S31).
 画像解析部120は、オブジェクトごとに算出して特徴量記憶部130に記録した各特徴量に対して、各特徴量と予め設定されたしきい値とを比較し、しきい値によるリジェクトをそれぞれ行う(ステップS12,S22,S32)。そして、しきい値を満足する3つの特徴量を持つオブジェクトのみを受精卵候補とみなす。なお、前述したように、コントラストの影響により特徴量が大きく変化しないように、輪郭内部のテクスチャ特徴量(特徴量3)については、全オブジェクト中における輝度平均の最大値に対する割合として扱う。 The image analysis unit 120 compares each feature quantity with a preset threshold value for each feature quantity calculated for each object and recorded in the feature quantity storage unit 130, and rejects each threshold value. Perform (Steps S12, S22, S32). Only objects having three feature quantities that satisfy the threshold are regarded as fertilized egg candidates. As described above, the texture feature amount (feature amount 3) inside the contour is treated as a ratio to the maximum value of the average luminance in all objects so that the feature amount does not change greatly due to the influence of contrast.
 続いて、所定の上下限値の範囲をフルスケールとして各特徴量に対してスケールの正規化を行って(ステップS5)、共通のスケールに変換された各特徴量をスコア1,2,3として求める(ステップS6)。ステップS7では、オブジェクトごとにスコア1,2,3の総和からなる総合スコアを算出し、総合スコアをソートする(得点の大きい順に並べ替える)。この総合スコアが最大となるラベルのオブジェクトを観察対象の受精卵と認識し(ステップS8)、最終的には、このラベルを持つオブジェクトが受精卵であるとの判定結果が出力部140から出力される(ステップS9)。 Subsequently, normalization of the scale is performed on each feature amount with a predetermined upper and lower limit range as a full scale (step S5), and each feature amount converted into a common scale is set as scores 1, 2, and 3. Obtained (step S6). In step S7, a total score composed of the sum of scores 1, 2, and 3 is calculated for each object, and the total scores are sorted (rearranged in descending order of scores). The object with the label having the maximum total score is recognized as a fertilized egg to be observed (step S8). Finally, a determination result that the object with this label is a fertilized egg is output from the output unit 140. (Step S9).
 出力部130から出力された判定結果は、操作盤7の表示パネル72に表示され、観察画像中で最も高い総合スコアを持つオブジェクトに受精卵を示す表示がされる。 The determination result output from the output unit 130 is displayed on the display panel 72 of the operation panel 7, and a display indicating a fertilized egg is displayed on the object having the highest overall score in the observation image.
 具体的な表示方法として、例えば、受精卵であることを示す記号(例えば「J」)を付加して表示したり、受精卵とそれ以外の異物とを異なる色相や輝度で表示したり、異物を塗りつぶして表示したり、異物を除去した画像を表示する等により、受精卵とそれ以外の異物とを判別して表示する、などのインターフェースが例示される。なお、出力部140から出力される上記のような判別データを、通信部65を介して外部接続されるコンピュータ等に送信して、同様の画像を表示させたり、受精卵の生育状態を観察するための基礎データとして用いたりするように構成することができる。 As a specific display method, for example, a symbol indicating that the egg is a fertilized egg (for example, “J”) is added, the fertilized egg is displayed with a different hue or brightness, and a foreign object is displayed. The interface is exemplified such that a fertilized egg and other foreign matters are discriminated and displayed by, for example, painting and displaying an image from which foreign matters have been removed. The discrimination data output from the output unit 140 is transmitted to a computer or the like externally connected via the communication unit 65 to display the same image or observe the growth state of the fertilized egg. For use as basic data.
 これにより、観察者は、表示パネル72に表示された画像や外部接続されたコンピュータ等のモニタに表示された画像を参照することにより、観察中の(または既に観察画像の取得を終了した)各画像に含まれるオブジェクトが受精卵であるか否かを直ちに判断することができる。また、このようにして受精卵とそれ以外の異物とが判別されたデータを用いることにより、受精卵の生育状態を効率的に観察することが可能になる。 Thus, the observer refers to the image displayed on the display panel 72 and the image displayed on a monitor such as an externally connected computer, so that each of the observers who are observing (or that has already finished obtaining the observed image) It is possible to immediately determine whether or not the object included in the image is a fertilized egg. In addition, by using the data in which the fertilized egg and other foreign matters are discriminated in this way, the growth state of the fertilized egg can be efficiently observed.
 次に、受精卵の製造方法について図11を追加参照して概要説明する。まず、ステップS110において、受精卵を培地ドロップDと共に培養容器10(ディッシュ10a)内に注入し、この培養容器10を受精卵の培養に適した環境条件に維持された培養室2内に収納して、当該環境条件の下で受精卵を培養する。なお、この環境条件は、制御ユニット6において培養室2内の温度や湿度、二酸化炭素濃度等が受精卵の培養環境に合わせて調節される。 Next, a method for producing a fertilized egg will be briefly described with reference to FIG. First, in step S110, the fertilized egg is injected into the culture container 10 (dish 10a) together with the medium drop D, and the culture container 10 is stored in the culture chamber 2 maintained at environmental conditions suitable for culturing the fertilized egg. Then, fertilized eggs are cultured under the environmental conditions. The environmental conditions are adjusted in the control unit 6 according to the culture environment of the fertilized egg such as the temperature, humidity, and carbon dioxide concentration in the culture chamber 2.
 ステップS120では、培養容器10内の受精卵の観察として、前述した画像処理のステップS1~S9(図1を参照)を実行して、観察画像に写し込まれる複数のオブジェクトの中から受精卵を識別する。このとき培養容器10(ディッシュ10a)内において、1個の培地ドロップDに対して受精卵が各1個ずつ識別される。 In step S120, as the observation of the fertilized egg in the culture container 10, the above-described image processing steps S1 to S9 (see FIG. 1) are executed, and a fertilized egg is selected from a plurality of objects copied in the observation image. Identify. At this time, one fertilized egg is identified for each medium drop D in the culture vessel 10 (dish 10a).
 続いて、ステップS130では、培地ドロップDごとに識別された複数の受精卵を所定の選別基準に基づいて選別する。受精卵の選別基準としては、卵割のタイミングや卵割球の形態等に基づいて受精卵のグレードが判定されて、この選別基準を満足する良好なものが選別される。例えば、良好な生育状態を経たものとして、卵内全ての卵細胞において卵割の起きたタイミングが同時期であるか否かに基づいて行われる。すなわち、正常な受精卵の卵割については、同じ世代の各細胞はほぼ同時期のタイミングで分裂し、胚内には同じ世代の細胞のみが存在する。一方、異常な受精卵の卵割については、同じ世代の細胞であっても分裂するタイミングがずれて、胚内には異なる世代の細胞が混在してしまう。 Subsequently, in step S130, a plurality of fertilized eggs identified for each medium drop D are selected based on a predetermined selection criterion. As a selection standard for a fertilized egg, the grade of the fertilized egg is determined based on the timing of cleavage, the shape of the blastomere, and the like, and a good one satisfying this selection standard is selected. For example, it is performed based on whether or not the timing at which cleavage occurs in all egg cells in the egg is in the same period as having passed through a good growth state. That is, regarding the cleavage of normal fertilized eggs, cells of the same generation divide at almost the same timing, and only cells of the same generation exist in the embryo. On the other hand, regarding the cleavage of an abnormal fertilized egg, even when cells are of the same generation, the division timing is shifted, and cells of different generations are mixed in the embryo.
 ステップS140では、上記選別した受精卵(胚盤胞と称される状態にまで成長した良好な受精卵)を採取して、例えばマイナス196℃の液体窒素の中で凍結保存する。そして、この受精卵(胚盤胞)は所定の周期のときに母体へ戻される(胚移植される)。なお、培養される受精卵は、ヒト、ウシ、ウマ、ブタ、マウス等の受精卵であってもよい。また、受精卵の保存は胚盤胞の状態で保存してもよいし、分割期(4細胞期胚、8細胞期胚)の状態で保存してもよい。 In step S140, the selected fertilized eggs (good fertilized eggs that have grown to a state called a blastocyst) are collected and stored frozen, for example, in liquid nitrogen at minus 196 ° C. The fertilized egg (blastocyst) is returned to the mother (embryo transfer) at a predetermined cycle. The fertilized eggs to be cultured may be fertilized eggs such as humans, cows, horses, pigs and mice. In addition, the fertilized egg may be stored in a blastocyst state or may be stored in a divided phase (4-cell stage embryo, 8-cell stage embryo).
 以上説明したように、本実施形態の画像処理プログラムGP、この画像処理プログラムGPが実行されることにより構成される受精卵観察の画像処理方法及び画像処理装置100、並びに受精卵の製造方法によれば、撮像した観察画像内に写し込まれる複数のオブジェクトの中から観察対象である受精卵を的確に認識することが可能である。 As described above, according to the image processing program GP of the present embodiment, the image processing method and image processing apparatus 100 for observing a fertilized egg configured by executing the image processing program GP, and the method for manufacturing a fertilized egg For example, it is possible to accurately recognize a fertilized egg that is an observation target from among a plurality of objects copied in the captured observation image.
 なお、上述の実施形態では、輪郭の円形度、輪郭の外側と内側の輝度差、及び輪郭内部のテクチャ特徴量、の3つの特徴量に基づいて受精卵を認識する処理方法を例示したが、本発明はこの実施形態に限定されるものではなく、他の特徴量(形状特徴量やテクスチャ特徴量など)に基づいて受精卵を認識する方法や、更に別の特徴量を付加して4つ、5つ、…の特徴量に基づいて受精卵を認識する方法に適用しても、同様の効果を得ることができる。例えば、観察倍率の比較的低い画像に基づいてオブジェクトの輪郭部の輝度値や、オブジェクト内部の輝度の分散値などを新たな特徴量として、上記3つの特徴量のうちの1つと入れ替えて用いた構成や、上記3つの特徴量に付加して4つ、5つの特徴量として構成してもよい。 In the above-described embodiment, the processing method for recognizing a fertilized egg based on the three feature amounts of the circularity of the contour, the luminance difference between the outside and inside of the contour, and the texture feature amount inside the contour is illustrated. The present invention is not limited to this embodiment, and a method for recognizing a fertilized egg based on other feature quantities (such as shape feature quantities and texture feature quantities), and four additional feature quantities are added. Even when applied to a method for recognizing a fertilized egg based on the five feature quantities, the same effect can be obtained. For example, based on an image with a relatively low observation magnification, the brightness value of the outline of the object, the variance value of the brightness inside the object, and the like are used as new feature values, replacing one of the above three feature values. The configuration may be configured as four or five feature amounts in addition to the above three feature amounts.
 また、上述したように、本実施形態に例示する観察ユニット5の顕微観察系55においては、観察倍率が対物レンズ等のレンズ設定に応じて変倍可能に構成されており、観察画像から求められる特徴量には、低倍画像(低倍位相差画像)においてより顕著に現れる特徴量と、高倍画像(高倍位相差画像)においてより顕著に現れる特徴量とがある。本実施形態で例示した3つの特徴量については、数値化やテクスチャの検出が可能なように解像度の高い高倍画像に基づいて求められるのが望ましく、上記した新たな特徴量としての輪郭内部の輝度値については、輝度変化が顕著に現れる低倍画像に基づいて求められるのが望ましい。ここで、高倍画像とは例えば観察倍率10倍や20倍などの画像であり、低倍画像とは例えば観察倍率2倍程度の画像であり、観察対象の受精卵、卵細胞、胚などの大きさ等に応じて適宜な倍率を用いることができる。 Further, as described above, in the microscopic observation system 55 of the observation unit 5 exemplified in this embodiment, the observation magnification is configured to be variable according to lens settings such as an objective lens, and is obtained from the observation image. The feature amount includes a feature amount that appears more prominently in a low-magnification image (low-magnification phase difference image) and a feature amount that appears more prominently in a high-magnification image (high-magnification phase difference image). The three feature quantities exemplified in the present embodiment are preferably obtained based on a high-resolution high-magnification image so that numerical values and textures can be detected, and the brightness inside the contour as the new feature quantities described above. The value is preferably obtained based on a low-magnification image in which a luminance change is noticeable. Here, the high-magnification image is an image having an observation magnification of 10 or 20 times, for example, and the low-magnification image is an image having an observation magnification of about 2 times, for example, the size of a fertilized egg, egg cell, embryo, or the like to be observed An appropriate magnification can be used according to the above.
 さらに、上述の実施形態では、培地ドロップD内に注入される1個の受精卵aを識別処理する構成を例示したが、本発明はこの実施形態に限定されるものではなく、培地ドロップDに注入される複数の受精卵a全てを識別するように構成してもよい。例えば、総合スコアに対して、予め受精卵識別のしきい値を設定しておき、このしきい値を超えるオブジェクトを受精卵であると判別したり、培地ドロップDに注入される受精卵aの個数だけ予め設定しておき、その個数に応じた総合スコアの上位のオブジェクトを受精卵であると認識したりする構成としてもよい。 Furthermore, in the above-described embodiment, the configuration for identifying and processing one fertilized egg a injected into the medium drop D is exemplified, but the present invention is not limited to this embodiment, You may comprise so that all the several fertilized eggs a inject | poured may be identified. For example, a threshold value for identifying a fertilized egg is set in advance for the total score, and an object exceeding this threshold value is determined to be a fertilized egg, or the fertilized egg a injected into the medium drop D A configuration may be adopted in which only the number is set in advance, and an object having a higher overall score corresponding to the number is recognized as a fertilized egg.
BS 培養観察システム        GP 画像処理プログラム
a 受精卵              5 観察ユニット
6 制御ユニット           7 操作盤
54 マクロ観察系          54c 撮像装置
55 顕微観察系           55c 撮像装置
61 CPU             62 ROM
63 RAM             100 画像処理装置
120 画像解析部          140 出力部
BS culture observation system GP image processing program a fertilized egg 5 observation unit 6 control unit 7 operation panel 54 macro observation system 54c imaging device 55 micro observation system 55c imaging device 61 CPU 62 ROM
63 RAM 100 Image Processing Device 120 Image Analysis Unit 140 Output Unit

Claims (18)

  1.  観察視野内に位置する複数の物体を撮像装置により撮影した観察画像を取得し、
     前記観察画像に写し込まれた前記複数の物体を抽出し、
     前記観察画像に含まれる前記物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、
     算出された前記複数の特徴量に基づいて、前記複数の物体の中から受精卵を識別することを特徴とする受精卵観察の画像処理方法。
    Acquire an observation image obtained by photographing a plurality of objects located in the observation visual field with an imaging device,
    Extracting the plurality of objects imprinted in the observation image;
    For each of the objects included in the observed image, calculate a plurality of image feature amounts according to the fertilized egg attributes,
    A fertilized egg observation image processing method, wherein a fertilized egg is identified from the plurality of objects based on the plurality of calculated feature quantities.
  2.  前記受精卵の属性が、前記受精卵における輪郭部及び輪郭領域内の構造に関するものであることを特徴とする請求項1に記載の受精卵観察の画像処理方法。 2. The fertilized egg observation image processing method according to claim 1, wherein the attribute of the fertilized egg relates to a contour portion and a structure in the contour region of the fertilized egg.
  3.  前記複数の特徴量が、前記物体における輪郭部の円形状の度合い示す画像特徴量を含むことを特徴とする請求項1または2に記載の受精卵観察の画像処理方法。 The image processing method for fertilized egg observation according to claim 1 or 2, wherein the plurality of feature amounts include an image feature amount indicating a degree of a circular shape of the contour portion of the object.
  4.  前記複数の特徴量が、前記物体における輪郭領域内の帯状部分の輝度に基づく画像特徴量を含むことを特徴とする請求項1または2に記載の受精卵観察の画像処理方法。 The image processing method for fertilized egg observation according to claim 1 or 2, wherein the plurality of feature amounts include an image feature amount based on luminance of a band-shaped portion in a contour region of the object.
  5.  前記複数の特徴量が、前記物体における輪郭領域内のテクスチャ構造に基づく画像特徴量を含むことを特徴とする請求項1または2に記載の受精卵観察の画像処理方法。 3. The fertilized egg observation image processing method according to claim 1, wherein the plurality of feature amounts include image feature amounts based on a texture structure in a contour region of the object.
  6.  コンピュータにより読み取り可能であり、撮像装置により撮影されて画像を取得して画像処理する画像処理装置として前記コンピュータを機能させるための画像処理プログラムであって、
     前記撮像装置により視野内に位置する複数の物体を撮影した観察画像を取得するステップと、
     前記観察画像に写し込まれた前記複数の物体を抽出するステップと、
     前記観察画像に含まれる前記物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出するステップと、
     算出された前記複数の特徴量に基づいて、前記複数の物体の中から受精卵を識別するステップと、
     前記物体に対する識別結果を出力するステップとを
    前記コンピュータに実現させることを特徴とする受精卵観察の画像処理プログラム。
    An image processing program for causing a computer to function as an image processing apparatus that is readable by a computer, acquires an image captured by an imaging device, and performs image processing,
    Obtaining an observation image obtained by photographing a plurality of objects located within a visual field by the imaging device;
    Extracting the plurality of objects imprinted in the observation image;
    For each of the objects included in the observation image, calculating a plurality of image feature amounts according to fertilized egg attributes;
    Identifying a fertilized egg from the plurality of objects based on the calculated feature quantities;
    An image processing program for observing a fertilized egg, wherein the computer realizes the step of outputting the identification result for the object.
  7.  前記受精卵の属性が、前記受精卵における輪郭部及び輪郭領域内の構造に関するものであることを特徴とする請求項6に記載の受精卵観察の画像処理プログラム。 The fertilized egg observation image processing program according to claim 6, wherein the attributes of the fertilized egg relate to a structure in a contour portion and a contour region of the fertilized egg.
  8.  前記複数の特徴量が、前記物体における輪郭部の円形状の度合い示す画像特徴量を含むことを特徴とする請求項6または7に記載の受精卵観察の画像処理プログラム。 The image processing program for fertilized egg observation according to claim 6 or 7, wherein the plurality of feature amounts include an image feature amount indicating a degree of a circular shape of a contour portion of the object.
  9.  前記複数の特徴量が、前記物体における輪郭領域内の帯状部分の輝度に基づく画像特徴量を含むことを特徴とする請求項6または7に記載の受精卵観察の画像処理プログラム。 The image processing program for fertilized egg observation according to claim 6 or 7, wherein the plurality of feature amounts include an image feature amount based on luminance of a band-shaped portion in a contour region of the object.
  10.  前記複数の特徴量が、前記物体における輪郭領域内のテクスチャ構造に基づく画像特徴量を含むことを特徴とする請求項6または7に記載の受精卵観察の画像処理プログラム。 The image processing program for fertilized egg observation according to claim 6 or 7, wherein the plurality of feature amounts include image feature amounts based on a texture structure in a contour region of the object.
  11.  複数の物体を撮影する撮像装置と、
     前記撮像装置により撮影された観察画像から前記複数の物体を抽出し、前記複数の物体の中から受精卵を識別する画像解析部と、
     前記画像解析部により判断された識別結果を外部に出力する出力部とを備え、
     前記画像解析部が、前記観察画像に含まれる前記物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、算出された前記複数の特徴量に基づいて、前記複数の物体の中から受精卵を識別するように構成したことを特徴とする受精卵観察の画像処理装置。
    An imaging device for photographing a plurality of objects;
    Extracting the plurality of objects from the observation image photographed by the imaging device, and an image analysis unit for identifying a fertilized egg from the plurality of objects;
    An output unit for outputting the identification result determined by the image analysis unit to the outside,
    The image analysis unit calculates a plurality of image feature amounts according to the attributes of the fertilized egg for each object included in the observation image, and based on the calculated feature amounts, the plurality of object features An image processing apparatus for observing a fertilized egg, characterized in that the fertilized egg is identified from the inside.
  12.  前記受精卵の属性が、前記受精卵における輪郭部及び輪郭領域内の構造に関するものであることを特徴とする請求項11に記載の受精卵観察の画像処理装置。 12. The fertilized egg observation image processing apparatus according to claim 11, wherein the attribute of the fertilized egg relates to a structure in a contour portion and a contour region of the fertilized egg.
  13.  前記複数の特徴量が、前記物体における輪郭部の円形状の度合い示す画像特徴量を含むことを特徴とする請求項11または12に記載の受精卵観察の画像処理装置。 The image processing apparatus for fertilized egg observation according to claim 11 or 12, wherein the plurality of feature amounts include an image feature amount indicating a degree of a circular shape of a contour portion of the object.
  14.  前記複数の特徴量が、前記物体における輪郭領域内の帯状部分の輝度に基づく画像特徴量を含むことを特徴とする請求項11または12に記載の受精卵観察の画像処理装置。 The image processing apparatus for fertilized egg observation according to claim 11 or 12, wherein the plurality of feature quantities include an image feature quantity based on a luminance of a band-shaped portion in a contour region of the object.
  15.  前記複数の特徴量が、前記物体における輪郭領域内のテクスチャ構造に基づく画像特徴量を含むことを特徴とする請求項11または12に記載の受精卵観察の画像処理装置。 The image processing apparatus for fertilized egg observation according to claim 11 or 12, wherein the plurality of feature amounts include image feature amounts based on a texture structure in a contour region of the object.
  16.  所定の環境条件で受精卵を培養し、
     受精卵が存在する培養容器中から、請求項11~15のいずれかに記載の画像処理装置を用いて受精卵を識別することを特徴とする受精卵の製造方法。
    Culturing fertilized eggs under the specified environmental conditions,
    A method for producing a fertilized egg, wherein the fertilized egg is identified from the culture container in which the fertilized egg exists using the image processing apparatus according to any one of claims 11 to 15.
  17.  所定の環境条件で受精卵を培養し、
     受精卵が存在する培養容器中において、観察視野内に位置する複数の物体を撮像装置により撮影した観察画像を取得し、
     前記観察画像に写し込まれた前記複数の物体を抽出し、
     前記観察画像に含まれる前記物体ごとに、受精卵の属性に応じた画像の特徴量を複数算出し、
     算出された前記複数の特徴量に基づいて、前記培養容器中の前記複数の物体の中から受精卵を識別することを特徴とする受精卵の製造方法。
    Culturing fertilized eggs under the specified environmental conditions,
    In the culture container in which the fertilized egg exists, obtain an observation image obtained by photographing a plurality of objects located in the observation visual field with an imaging device,
    Extracting the plurality of objects imprinted in the observation image;
    For each of the objects included in the observed image, calculate a plurality of image feature amounts according to the fertilized egg attributes,
    A fertilized egg manufacturing method, wherein a fertilized egg is identified from the plurality of objects in the culture container based on the plurality of calculated feature quantities.
  18.  識別された受精卵を所定の選別基準に基づいて選別し、
     選別された受精卵を前記培養容器中から採取して保存することを特徴とする請求項16又は17に記載の受精卵の製造方法。
    Screening the identified fertilized eggs based on a predetermined screening standard,
    18. The method for producing a fertilized egg according to claim 16 or 17, wherein the selected fertilized egg is collected from the culture container and stored.
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