WO2009119329A1 - 細胞観察画像の画像解析方法、画像処理プログラム及び画像処理装置 - Google Patents
細胞観察画像の画像解析方法、画像処理プログラム及び画像処理装置 Download PDFInfo
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- WO2009119329A1 WO2009119329A1 PCT/JP2009/054757 JP2009054757W WO2009119329A1 WO 2009119329 A1 WO2009119329 A1 WO 2009119329A1 JP 2009054757 W JP2009054757 W JP 2009054757W WO 2009119329 A1 WO2009119329 A1 WO 2009119329A1
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
- C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
- C12M41/12—Means for regulation, monitoring, measurement or control, e.g. flow regulation of temperature
- C12M41/14—Incubators; Climatic chambers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/693—Acquisition
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
- C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
- C12M41/46—Means for regulation, monitoring, measurement or control, e.g. flow regulation of cellular or enzymatic activity or functionality, e.g. cell viability
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
- C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
- C12M41/48—Automatic or computerized control
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
- G06T7/251—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
Definitions
- the present invention relates to a cell observation image processing means for deriving a predicted movement direction of a cell.
- a movement prediction means for a moving object As a movement prediction means for a moving object (moving body), an image processing technique for predicting how the moving body will move in the future by processing and analyzing an image obtained by capturing the moving body has been widely used. ing. For such movement prediction, time series prediction based on past movement amounts such as linear prediction and Kalman filter is used (see, for example, Patent Document 1). JP 2007-303886 A
- the present invention has been made in view of the above problems, and an object of the present invention is to provide means capable of realizing cell migration prediction with a simple configuration.
- an observation image in which observation cells are photographed by an imaging device is acquired, the contour of the observation cell is extracted from the acquired observation image, and the observation is extracted from the contour
- an observation image obtained by photographing an observation cell is acquired by an imaging device, the outline of the observation cell is extracted from the acquired observation image, and the observation is extracted from the outline
- a method for analyzing an image of a cell observation image comprising: calculating a deviation of an internal structure with respect to a contour shape of a cell, and deriving a movement direction in which the observation cell is predicted to move based on the calculated deviation of the internal structure Is provided.
- the step of acquiring an observation image obtained by photographing an observation cell by an imaging device the step of extracting the outline of the observation cell from the acquired observation image, Adapting a cell model obtained by modeling an outer shape of a cell to the extracted observation cell; deriving a movement direction in which the observation cell is predicted to move using the adapted cell model; And a step of outputting the derived moving direction of the observation cell to the outside.
- An image processing program for a cell observation image is provided.
- a step of acquiring an observation image obtained by photographing an observation cell by an imaging device a step of extracting an outline of the observation cell from the acquired observation image, and an outline extraction Calculating a bias of the internal structure with respect to the contour shape of the observed cell, deriving a movement direction in which the observation cell is predicted to move based on the calculated bias of the internal structure, and the derived
- an image processing program for a cell observation image comprising a step of outputting the moving direction of the observation cell to the outside.
- an imaging device that images a cell and an observation image obtained by imaging the observation cell by the imaging device are acquired to derive a movement direction in which the observation cell is predicted to move.
- an output unit that outputs the moving direction of the observation cell derived by the image analysis unit to the outside, and the image analysis unit extracts the outline of the observation cell from the observation image
- a cell model obtained by modeling the outer shape of a cell is adapted to the observation cell from which a contour has been extracted, and a moving direction in which the observation cell is predicted to move is derived using the adapted cell model.
- An image processing apparatus for observing cells is provided.
- an imaging device that images a cell, and an observation image obtained by imaging the observation cell by the imaging device is acquired, and a movement direction that is predicted to move the observation cell is derived.
- an output unit that outputs the moving direction of the observation cell derived by the image analysis unit to the outside, and the image analysis unit extracts the outline of the observation cell from the observation image, It is configured to calculate a bias of the internal structure with respect to the contour shape of the observation cell from which the contour is extracted, and to derive a movement direction in which the observation cell is predicted to move based on the calculated bias of the internal structure.
- An image processing apparatus for cell observation is provided.
- the moving direction of the cells can be predicted from one image taken by the imaging apparatus. Therefore, it is possible to provide a means capable of realizing cell movement prediction with a simple configuration.
- FIG. 1 is a block diagram illustrating a schematic configuration of an image processing apparatus. It is a flowchart which shows the main flow in an image processing program. It is a flowchart corresponding to the prediction algorithm A selected in the main flow. It is a flowchart corresponding to the prediction algorithm B selected in the main flow. It is a flowchart corresponding to the prediction algorithm C selected in the main flow. It is a structural example of the display image of the cell movement tracking interface displayed on a display panel when an image processing program is executed.
- FIGS. 2 and 3 As an example of a system to which the cell observation image processing apparatus of the present invention is applied, a schematic configuration diagram and a block diagram of a culture observation system are shown in FIGS. 2 and 3, respectively.
- This culture observation system BS is broadly divided into a culture chamber 2 provided at the top of the housing 1, a shelf-like stocker 3 that accommodates and holds a plurality of culture containers 10, and a sample in the culture container 10. From an observation unit 5 for observation, a transport unit 4 for transporting the culture vessel 10 between the stocker 3 and the observation unit 5, a control unit 6 for controlling the operation of the system, an operation panel 7 equipped with an image display device, etc. Composed.
- the culture room 2 is a room for forming and maintaining a culture environment according to the type and purpose of the cells to be cultured, and is kept sealed after the sample is charged in order to prevent environmental changes and contamination.
- a temperature adjustment device 21 for raising and lowering the temperature in the culture chamber
- a humidifier 22 for adjusting the humidity
- a gas supply device 23 for supplying a gas such as CO 2 gas and N 2 gas
- the culture A circulation fan 24 for making the environment of the entire chamber 2 uniform, an environmental sensor 25 for detecting the temperature, humidity and the like 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 has a type such as a flask, a dish, and a well plate, a form such as a round shape and a square shape, and a size, and an appropriate one can be selected and used according to the type and purpose of the cell to be cultured. .
- a configuration using a dish is illustrated.
- Samples such as cells are injected into the culture vessel 10 together with a liquid medium containing a pH indicator such as phenol red.
- the culture container 10 is assigned a code number and is stored in association with the designated address of the stocker 3.
- the culture container 10 is housed and held on each shelf in a state where a container holder for transportation formed according to the type and form of the container is mounted.
- 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.
- 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, etc.
- a support arm 45 for lifting and supporting the culture vessel 10 is provided on the distal end side.
- 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 sample table 15 of 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.
- the observation unit 5 includes a first illumination unit 51, a second illumination unit 52, and a third illumination unit 53, a macro observation system 54 that performs macro observation of the sample, a micro observation system 55 that performs micro observation of the sample, and an image processing apparatus. 100 or the like.
- the sample stage 15 is made of a material having translucency, and a transparent window portion 16 is provided in the observation region 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 microscope observation system 5 from above the sample stage 15. Illuminate the sample in the culture vessel along the axis.
- the third illumination unit 53 includes a plurality of light sources such as LEDs and mercury lamps that emit light having a wavelength suitable for epi-illumination observation and fluorescence observation, and the light emitted from each light source as an optical axis of the microscopic observation system 55. And an illumination optical system composed of a beam splitter, a fluorescent filter, and the like to be superposed, disposed in the lower frame 1b located on the lower side of the culture chamber 2, and from the lower side of the sample stage 15 to the microscopic observation system 5. Illuminate the sample in the culture vessel along the optical axis.
- light sources such as LEDs and mercury lamps that emit light having a wavelength suitable for epi-illumination observation and fluorescence observation, and the light emitted from each light source as an 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 superposed, disposed in the lower frame 1b located on the lower side of the culture chamber 2, and from the lower side of the
- the macro observation system 54 includes an observation optical system 54a and an imaging device 54c such as a CCD camera that takes an image of the sample imaged by the observation optical system.
- the macro observation system 54 is located above the first illumination unit 51 and is a culture chamber. 2 is provided.
- the macro observation system 54 captures a whole 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.
- 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). For example, zooming is possible in the range of 2 to 80 times.
- the microscopic observation system 55 is transmitted light that has been illuminated by the second illumination unit 52 and transmitted through the cell, reflected light that has been illuminated by the third illumination unit 53 and reflected by the cell, or illuminated by the third illumination unit 53.
- a microscopic image (micro image) obtained by microscopic observation of fluorescence emitted by the cells is taken.
- the image processing apparatus 100 performs A / D conversion on signals input from the macro observation system imaging device 54c and the micro observation system imaging device 55c, and performs various image processing to generate an image of the entire observation image or the micro observation image. Generate data. Further, the image processing apparatus 100 performs image analysis on the image data of these observation images, and performs generation of a time-lapse image, calculation of the amount of cell movement, analysis of the movement state of the cell, and the like. Specifically, the image processing device 100 is constructed by executing an image processing program stored in the ROM of the control device 6 described below. The image processing apparatus 100 will be described in detail later.
- the control unit 6 includes a CPU 61, a ROM 62 in which data for controlling the operation of the culture observation system BS and data for controlling each unit are set and stored, a RAM 63 in which image data and the like are temporarily stored, and the like. They are connected by a data bus.
- the input / output port of the control unit 6 includes a temperature adjustment device 21 in the culture chamber 2, a humidifier 22, a gas supply device 23, a circulation fan 24 and an environmental sensor 25, and X, Y, Z stages 43, 42 in the transfer device 4.
- the operation panel 7 includes an operation panel 71 provided with input / output devices such as a read / write device for reading and writing information from a keyboard, a sheet switch, a magnetic recording medium, an optical disk, and the like, various operation screens, image data, and the like. And a display panel 72 for displaying the information, and setting the observation program (operating conditions), selecting conditions, operating commands, and the like on the operation panel 71 while referring to the display panel 72, thereby culturing through the CPU 61. Each part of the observation system BS is operated.
- the CPU 61 adjusts the environment of the culture chamber 2 according to the input from the operation panel 71, transports the culture vessel 10 in the culture chamber 2, observes the sample by the observation unit 5, analyzes the acquired image data, and displays the display panel. Display to 72 is executed. On the display panel 72, in addition to input screens for operation commands, condition selection, and the like, numerical values of environmental conditions of the culture chamber 2, analyzed image data, a warning when an abnormality occurs, and the like are displayed.
- the CPU 61 can transmit and receive data to and from an externally connected computer or the like via a communication unit 65 configured in accordance with a wired or wireless communication standard.
- the RAM 63 operating conditions of the observation program set on the operation panel 71, for example, environmental conditions such as temperature and humidity of the culture chamber 2, observation schedule for each culture vessel 10, observation type and observation position in the observation unit 5, observation Observation conditions such as magnification are recorded.
- the management data of the culture container 10 such as the code number of each culture container 10 accommodated in the culture chamber 2, the storage address of the stocker 3 in which the culture container 10 of each code number is accommodated, and various data used for image analysis are stored.
- the RAM 63 is provided with an image data storage area for recording image data photographed by the observation unit 5, and each image data is recorded in association with a code number of the culture vessel 10 and index data including the photographing date and time. Is done.
- 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, operates the driving mechanism of each axis of the X, Y, and Z stages 43, 42, and 41 of the transport unit 4 based on the observation schedule, and the observation target from the stocker 3.
- the culture container 10 is transported to the sample stage 15 of the observation 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 device 6 is processed by the image processing device 100 to generate a whole observation image, and the image data is recorded in the RAM 63 together with index data such as the shooting date and time.
- the specific position of the culture container 10 that has been transported by the transport unit 4 is set to the optical axis of the microscopic observation system 55.
- the light source of the second illumination unit 52 or the third illumination unit 53 is turned on, and the microscopic observation image by transmitted illumination, epi-illumination, and fluorescence is photographed by the imaging device 55c.
- a signal photographed by the imaging device 55c and inputted to the control device 6 is processed by the image processing device 100 to generate a microscopic observation image, and the image data is recorded in the RAM 63 together with index data such as photographing date and time. .
- the CPU 61 performs the observation as described above on the plurality of culture container samples accommodated in the stocker 3 according to the observation program at an interval of about 30 minutes to 2 hours based on the observation program.
- the photographing time interval may be constant or different.
- the image data of the photographed whole observation image and microscopic observation image are recorded in the image data storage area of the RAM 63 together with the code number of the culture vessel 10.
- the image data recorded in the RAM 63 is read from the RAM 63 in response to an image display command input from the operation panel 71, and an entire observation image or a microscopic observation image (single image) at a specified time or an entire observation in a specified time region.
- An image or a time-lapse image of a microscopic observation image is displayed on the display panel 72 of the operation panel 7.
- the image processing apparatus 100 has a function of predicting the moving direction of cells in addition to functions such as time-lapse image generation and cell tracking.
- a method for predicting cell movement a method for predicting movement by extracting a shape feature of an observation cell from an image of a cell to be observed (observation cell), and We present a method of movement prediction that extracts the characteristics of the internal structure of the observation cell and performs movement prediction.
- I movement prediction based on shape features
- II movement prediction based on features of internal structure will be described from the basic concept.
- FIG. 4 is a schematic view illustrating the situation of the outermost contour extraction processing.
- the image (a) acquired by the imaging device 55c (54c) is subjected to image processing, and the outermost cell is shown in FIG. 4 (b). Extract the outline of.
- contour extraction processing for example, binarization using luminance values, binarization using variance values, and dynamic contour methods such as Snakes and Level Set methods can be used.
- a cell to be observed for movement prediction is referred to as an “observed cell”.
- a cell model that models the outer shape of the cell is applied to the observation cell from which the outermost contour has been extracted by preprocessing, and the observation cell moves using the adapted cell model Then, the predicted moving direction is derived.
- the direction of movement is derived based on (1) a prediction method based on the shape characteristics of the cell model adapted to the observed cell, and (2) a cell model adapted to the observed cell. Prediction method based on the deviation of the center of gravity position of the observation cell from which the center of gravity position and contour were extracted, (3) Prediction method based on the deviation of the contour shape of the cell model adapted to the observation cell and the outline shape of the observation cell suggest.
- the adaptation of the cell model to the observation cell is performed by, for example, the outermost of the observation cell C extracted by the preprocessing as shown in FIGS. 5, 6 and 1 in which the elliptical cell model Mc is applied. Approximate the contour to an elliptic model. Examples of the elliptic approximation here include a least square method and a method by moment calculation. Or you may estimate the ellipse model with the highest correlation with what filled the inside of the outline of the observation cell C. FIG. A predicted direction of cell movement is estimated using this cell model Mc.
- the major axis direction (arrow direction in FIG. 5) of the ellipse of the cell model Mc is used as the predicted movement direction (predicted movement direction) of the observation cell C.
- the probability that the moving direction of the cell is generally the longitudinal direction, that is, the major axis direction of the approximated ellipse is high.
- the movement direction is estimated using the relationship between the shape feature of the cell and the movement direction.
- the prediction method performed based on the shift between the center of gravity position of the cell model and the center of gravity of the observation cell applies the cell model Mc to the observation cell C as shown in FIG.
- the center-of-gravity position G is obtained from the shape by graphic processing, and the moving direction of the observation cell is derived from the deviation between the elliptical center O of the cell model Mc and the gravity center position G of the observation cell C.
- the moving direction is estimated using the feature that the center of gravity of the cell is biased in the moving direction (or the reverse direction). For example, as shown in FIG. 6, when the center of gravity G of the observed cell is biased to the right from the center O of the cell model Mc approximated by an ellipse, the biased direction of the center of gravity G can be derived as the predicted movement direction.
- the prediction method performed based on the deviation between the contour shape of the cell model and the contour shape of the observation cell is a more complicated case.
- the cell contour is complicated with respect to the elliptical cell model Mc.
- the direction in which the portion with the largest protrusion exists is the direction in which the observation cell C moves, or the opposite direction across the ellipse center O is observed.
- the direction in which the cell C moves is predicted.
- the cell utilizes a feature that stretches a part of the body (leg) when the cell moves, or a feature that an adhesive surface remains when the cell moves. Note that the direction of travel is determined by the cell type.
- the elliptical shape was illustrated as an example of the configuration of the cell model Mc, an appropriate shape can be used according to the morphological characteristics of the cell to be observed, for example, a triangle, a rectangle, a star, an arc, etc. Illustrated.
- the movement prediction based on the characteristics of the internal structure of the cell calculates the bias of the internal structure with respect to the contour shape of the observed cell from which the outermost contour was extracted by preprocessing, and the observed cell moves based on the calculated bias of the internal structure Then, the predicted moving direction is derived.
- Measures based on the density inside the cells can be cited as a way to capture the bias in the internal structure of the observed cells.
- the cell density can be expressed by the dispersion of luminance values inside the cell outline in the microscopic observation image. Therefore, the cell density in the cell contour is obtained from the variance of the luminance values, and the direction from the low cell density region to the high region is calculated as shown in FIG. And
- This movement prediction method estimates the predicted direction of cell movement by utilizing the tendency to move the internal tissue in the moving direction or vice versa when the cell moves. The relationship between the density change direction and the cell movement direction is determined by the cell type.
- a technique for capturing the bias of the internal structure of the observation cell a technique based on the texture characteristics of the observation cell can be mentioned. Textures such as nuclei and internal fibers exist inside the cell, and these internal structures move or change during movement. Therefore, in this movement prediction method, the texture inside the cell outline is obtained by a dispersion filter or the like, and the movement direction is predicted from, for example, the position of the nucleus in the cell outline or the direction in which the internal fibers extend.
- FIG. 8 is a block diagram showing a schematic configuration of the image processing apparatus 100 that executes image processing for movement prediction
- FIG. 9 is a flowchart showing a main flow in the image processing program GP for movement prediction
- FIGS. It is a flowchart corresponding to the prediction algorithms A, B, and C selected in the flow.
- the image processing apparatus 100 includes an image analysis unit 120 that obtains an observation image obtained by capturing the observation cell C by the imaging device 55c (54c) and derives a movement direction in which the observation cell is predicted to move, and an image analysis unit 120.
- An output unit 130 that outputs the derived movement direction of the observed cell C to the outside, and is configured to output and display the predicted movement direction derived by the image analysis unit 120 on the display panel 72, for example.
- 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 image analysis unit 120 extracts the outline of the observation cell C from the acquired observation image, and the observation cell C from which the outline is extracted.
- the cell model Mc in which the outer shape of the cell is modeled is adapted to be used, and the predicted movement direction of the observation cell C is derived using the adapted cell model Mc (see also FIGS. 4 to 6 and FIG. 1). .
- the image analysis unit 120 extracts the outline of the observation cell C from the acquired observation image, and the observation cell C from which the outline is extracted is extracted.
- the deviation of the internal structure with respect to the contour shape is calculated, and the movement direction in which the observation cell C is predicted to move is derived based on the calculated deviation of the internal structure (see also FIGS. 4 and 7).
- the image analysis processing by the image analysis unit 120 as described above can be executed by reading out image data in which an observation image including the observation cell C is already stored in the RAM 63, and an image of a cell to be observed from now on is imaged. It is also possible to obtain and execute by Therefore, in this embodiment, a case where the current image is acquired and the movement prediction is performed will be described with reference to a display image configuration example of the movement prediction interface on the display panel 72 shown in FIG.
- FIG. 13 shows a state in which the cultured cell dish (culture vessel) having the code number Cell-0002 is selected by the cursor provided on the operation panel 71.
- the CPU 61 operates the driving mechanism of each axis of the transport unit 4 to transport the culture container 10 to be observed from the stocker 3 to the observation unit 5. Then, a microscopic observation image by the microscopic observation system 55 is photographed by the imaging device 55 c, and the image is displayed in the “observation position” frame 722.
- FIG. 13 shows a state in which the observer designates a shaded area from the center right using a mouse attached to the operation panel 71.
- an image of the region designated by the observer is acquired as an observation image by the image analysis unit 120 (step S1).
- the acquired observation image is instantaneously subjected to cell outermost contour extraction processing (segmentation) by the image analysis unit 120 (step S2), and the outermost contour is extracted in the “observation image” frame 723 of the display panel 72.
- a cell image is displayed. Therefore, as shown in FIG. 13, in the observation image, the observation cell (cell of interest) for which the movement direction is predicted is specified using a mouse or the like (step S3). Note that the observation cell may be designated as a cell that has already been detected by tracking or the like.
- a “movement prediction option” frame 724 is formed below the observation image frame 723, and a “movement prediction method” frame 725 is formed in the frame, and which prediction algorithm is applied to perform movement prediction, Selection buttons 725a, 725b, and 725c for the movement prediction method are displayed.
- A Prediction by outermost contour eccentricity direction, that is, I: center of gravity position O of cell model Mc and center of gravity G of observation cell C explained in FIG. 6 in (2) of movement prediction based on shape feature A method of predicting the direction of movement based on the deviation.
- B Prediction based on the protruding direction of the ellipse, that is, I: Based on the deviation between the contour shape of the cell model Mc and the contour shape of the observation cell C described in FIG. To predict the direction of movement.
- C Prediction based on cell tissue density direction. That is, II: A method of predicting the movement direction based on the characteristics of the internal structure of the observation cell C described in FIG.
- B A prediction method selection button 725b based on the protruding direction of the ellipse
- C cell tissue
- a selection button 725c for a prediction method based on the density direction is configured to be displayed on the main screen.
- step S4 the observer selects one of the selection buttons 725a, 725b, and 725c to select the prediction algorithms A, B, and C.
- step S5A outermost contour is selected.
- the flow branches to either the flow of the prediction algorithm based on the eccentric direction, S5B: the flow of the prediction algorithm based on the protruding direction of the ellipse, or the flow of the prediction algorithm based on the cell tissue density direction.
- step S11 the cell model Mc is approximated with respect to the outermost contour of the observation cell.
- the prediction algorithm is indicated by a dotted line in FIG.
- step S12 the centroid position (center position in the case of an ellipse) O of the cell model Mc and the centroid position G in the contour shape of the observation cell C are calculated, and the process proceeds to step S13.
- a “cell model selection” frame 726 is formed in the “movement prediction option” frame 724, and a selection button for selecting the movement characteristic of the observation cell C is displayed in this frame.
- the “cell internal movement type” selection button 726a for moving the center of gravity position or the “cell outline (foot) movement type” selection button 726b for extending a part of the body is selected.
- a database in which cell names (types, identification numbers, etc.) and movement characteristics are registered in advance is constructed, and the database search button 726c is selected and the observation cell name is input.
- an item for automatically determining the cell movement characteristics by searching the database is also provided.
- the input burden on the observer can be reduced, and erroneous selection can be prevented and the prediction accuracy of movement prediction can be improved.
- step S14 the detection of the moving direction is executed according to the moving characteristics of the cell selected in step S13.
- the positional relationship between the center O of the cell model Mc calculated in step S12 and the center of gravity G of the observation cell C is as shown in FIG. 6, and the center of gravity G of the observation cell is on the right side of the center O of the cell model Mc.
- the movement characteristic of the cell calculated to be located and selected in step S13 is a characteristic that biases the center of gravity G in the movement direction
- step S14 the direction from the elliptical center O of the cell model toward the center of gravity G of the observation cell. Is calculated.
- step S13 the movement characteristics of the cells selected in step S13 indicate that the center of gravity G is the moving direction. If the characteristic is biased in the reverse direction, in step S14, the direction from the center of gravity G of the observed cell toward the elliptical center O of the cell model is calculated. Then, the direction calculated in step S14 is determined as the movement prediction direction of the observation cell C in step S15, and the process returns to the main flow of movement prediction and proceeds to step S6.
- a “tracking target cell” frame 727 is formed on the display screen.
- a close-up display of the observation cell C designated in step S3 and the predicted movement direction of the observation cell C are represented by a vector. Displayed by display.
- the “all cell movement prediction vector display” selection button formed below the “observation image” frame 723 is set to ON. As shown in the figure, the movement vector is superimposed on each cell in the observation image and displayed.
- step S5B Flow of prediction algorithm based on oval protrusion direction
- the approximation process of the cell model Mc is performed on the outermost contour of the observation cell in step S21.
- An elliptical cell model Mc is applied to the cell C.
- step S22 the position and size of the observation cell C protruding from the cell model Mc are calculated, the azimuth having the maximum protrusion amount is calculated, and the process proceeds to step S23.
- a “cell model selection” frame 726 is formed in the “movement prediction option” frame 724 of the display screen, and a selection button for selecting the movement characteristic of the observation cell C is displayed.
- the observer selects one of the “cell internal movement type” selection button 726a and the “cell outline (foot) movement type” selection button 726b in accordance with the observation cell.
- the sub-screen (not shown) displayed corresponding to each selection button it is the type that biases the center of gravity in the cell movement direction or the reverse type (726a), the type that extends the leg or the type that the adhesive surface remains ( 726b) and the like are selected, whereby the movement characteristics of the observation cell C are designated.
- an item for automatically determining the cell migration characteristics is provided by constructing a database for the cell migration characteristics, selecting the database search button 726c, and inputting the name of the observation cell. deep.
- the input burden on the observer can be reduced, and erroneous selection can be prevented and the prediction accuracy of movement prediction can be improved.
- step S24 the movement direction is detected according to the movement characteristics of the cell selected in step S23.
- the maximum protruding portion of the observed cell C with respect to the cell model Mc calculated in step S22 is calculated to be at the lower left of the cell model Mc as shown in FIG. 1, and the movement of the cell selected in step S23 is performed.
- the characteristic is a characteristic in which the adhesive surface remains during movement
- a direction from the protruding position toward the center of the ellipse is calculated in step S24.
- the movement characteristic of the cell selected in step S23 is a characteristic of extending a leg in the movement direction during movement.
- step S24 the direction from the center of the ellipse toward the protruding position is calculated. Then, the direction calculated in step S24 is determined as the movement prediction direction of the observation cell C in step S25, and the process returns to the main flow of movement prediction and proceeds to step S6.
- step S6 a “tracking target cell” frame 727 is formed on the display screen, and the close-up display of the observation cell C designated in step S3 and the predicted movement direction of the observation cell are illustrated in this frame. It is displayed by such a vector display.
- the “all cell movement prediction vector display” selection button formed below the “observation image” frame 723 is set to ON. As shown in the figure, the movement vector is superimposed on each cell in the observation image and displayed.
- step S32 the distribution of luminance values in the observed cell contour is calculated to calculate the density distribution inside the cell.
- step S33 the distribution of the low to high cell density is calculated based on the structural features inside the cell, and the process proceeds to step S33.
- a “cell model selection” frame 726 is formed in the “movement prediction option” frame 724 of the display screen, and a selection button for selecting the movement characteristic of the observation cell C is displayed.
- a selection button for selecting the movement characteristic of the observation cell C is displayed.
- the “High Density Direction Movement Type” selection button that increases the density in the movement direction during movement or the “Low Density Direction Movement Type” selection button (both not shown) that decreases the density in the movement direction.
- Select As described above, a database for cell movement characteristics is constructed, and an item for automatically determining cell movement characteristics is provided by selecting the database search button 726c and inputting the name of the observation cell. Thus, the input burden on the observer can be reduced and erroneous selection can be prevented to improve the prediction accuracy of the movement prediction.
- step S34 detection of the moving direction is executed in accordance with the moving characteristics of the cell selected in step S33.
- the density distribution inside the cell calculated in step S32 is calculated such that the density is high in the upper right of the observation cell C and the density in the lower left is low, and the cell selected in step S33.
- a high-density direction moving type that moves in a high-density direction
- a direction from a low-density area to a high-density area is calculated in step S34.
- the cell movement characteristics selected in step S33 are the same density distribution but the low density direction moving type moves in the direction of lower density
- the density is increased from the high density region in step S34.
- a direction toward the low region is calculated.
- the direction calculated in step S34 is determined as the movement prediction direction of the observation cell C in step S35, and the process returns to the main flow of movement prediction and proceeds to step S6.
- a “tracking target cell” frame 727 is formed on the display screen.
- a close-up display of the observation cell C designated in step S3 and the predicted movement direction of the observation cell C are represented by a vector. Displayed by display.
- the “all cell movement prediction vector display” selection button formed below the “observation image” frame 723 is set to ON. As shown in the figure, the movement vector is superimposed on each cell in the observation image and displayed.
- a suitable prediction algorithm can be selected and applied according to the characteristics of the observation target, and when any prediction algorithm is applied, the observer sees the “tracking target cell” frame 727,
- the movement direction of the cell to be observed can be grasped in detail, and the movement state of the cell in the entire observation area is grasped by displaying the movement vector superimposed on each cell in the observation image in the “observation image” frame 723. be able to.
- the image analysis method and the image processing apparatus 100 configured by executing the image processing program, the image is captured and stored by the imaging apparatus.
- the cell moving direction can be predicted from the current or already recorded one image without reading and processing a large number of images. Accordingly, it is possible to provide means capable of performing prediction processing at high speed with a very simple configuration for predicting cell movement.
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Abstract
Description
GP 画像処理プログラム
C 観察細胞
54 マクロ観察系
54c 撮像装置
55 顕微観察系
55c 撮像装置
100 画像処理装置
120 画像解析部
130 出力部
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、タイムラプス画像の生成や細胞トラッキングなどの機能に加えて、細胞の移動方向を予測する機能を備えている。本明細書においては、細胞の移動予測を行う手法として、観察対象となる細胞(観察細胞)が撮影された画像から、観察細胞の形状特徴を抽出して移動予測を行う移動予測の手法と、観察細胞の内部構造の特徴を抽出して移動予測を行う移動予測の手法を提示する。以下、I:形状特徴に基づく移動予測、II:内部構造の特徴に基づく移動予測について、基本的な概念から説明する。
移動予測処理に先立って、細胞の最も外側の輪郭抽出を行う。図4は、この最外輪郭抽出処理の状況を例示する模式図であり、撮像装置55c(54c)によって取得された画像(a)を画像処理し、(b)に示すように細胞の最も外側の輪郭を抽出する。この輪郭抽出処理には、たとえば輝度値による2値化、分散値による2値化、SnakesやLevel Set法などの動的輪郭法などを利用することができる。なお、本明細書においては、移動予測を行う観察対象の細胞を「観察細胞」と表記する。
細胞の形状特徴に基づく移動予測は、前処理により最外輪郭が抽出された観察細胞に、細胞の外形形状をモデル化した細胞モデルを適応させ、適応した細胞モデルを利用して観察細胞が移動すると予測される移動方向を導出する。この移動予測に含まれる具体的な予測手法として、移動方向の導出を、(1)観察細胞に適応した細胞モデルの形状特徴に基づいて行う予測手法、(2)観察細胞に適応した細胞モデルの重心位置と輪郭が抽出された観察細胞の重心位置のずれに基づいて行う予測手法、(3)観察細胞に適応した細胞モデルの輪郭形状と観察細胞の輪郭形状のずれに基づいて行う予測手法を提案する。
細胞の内部構造の特徴に基づく移動予測は、前処理により最外輪郭が抽出された観察細胞の輪郭形状に対する内部構造の偏りを算出し、算出された内部構造の偏りに基づいて観察細胞が移動すると予測される移動方向を導出する。内部構造の偏りを検出する具体的な手法として、観察細胞の細胞密度に基づく手法、観察細胞のテクスチャ特徴に基づく手法を提案する。
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8~図12の各図を併せて参照しながら説明する。ここで、図8は移動予測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、図9は移動予測の画像処理プログラムGPにおけるメインフローを示すフローチャート、図10~図12はメインフローにおいて選択される予測アルゴリズムA,B,Cに対応したフローチャートである。
A:最外輪郭偏心方向による予測・・・すなわち、I:形状特徴に基づく移動予測の(2)で図6を用いて説明した、細胞モデルMcの重心位置Oと観察細胞Cの重心位置Gのずれに基づいて移動方向を予測する手法。
B:楕円はみ出し方向による予測・・・すなわち、I:形状特徴に基づく移動予測の(3)で図1を用いて説明した、細胞モデルMcの輪郭形状と観察細胞Cの輪郭形状のずれに基づいて移動方向を予測する手法。
C:細胞組織密度方向による予測・・・すなわち、II:内部構造の特徴に基づく移動予測で図7を用いて説明した、観察細胞Cの内部構造の特徴に基づいて移動方向を予測する手法。
の3種類の中から予測手法を選択設定可能に構成しており、A:最外輪郭偏心方向による予測手法の選択ボタン725a、B:楕円はみ出し方向による予測手法の選択ボタン725b、C:細胞組織密度方向による予測手法の選択ボタン725cがメイン画面に表示されるように構成している。
最外輪郭偏心方向による予測アルゴリズムは、図10に示すように、まず、ステップS11において、観察細胞の最外輪郭に対して細胞モデルMcの近似処理が実行され、例えば図6に点線で示したように、観察細胞Cに楕円形状の細胞モデルMcが適応される。次いで、ステップS12において、細胞モデルMcの重心位置(楕円の場合には中心位置)Oと、観察細胞Cの輪郭形状における重心位置Gとがそれぞれ算出され、ステップS13に進む。
楕円はみ出し方向による予測アルゴリズムは、図11に示すように、ステップS21において、観察細胞の最外輪郭に対して細胞モデルMcの近似処理が実行され、例えば図1に点線で示したように、観察細胞Cに楕円形状の細胞モデルMcが適応される。次いで、ステップS22において、細胞モデルMcからはみ出した観察細胞Cの位置と大きさが算出され、突出量が最大の方位が算出されて、ステップS23に進む。
細胞組織密度方向による予測アルゴリズムは、図12に示すように、まず、ステップS32において、観察細胞輪郭内の輝度値の分散を算出することにより細胞内部の密度分布を算出する。これにより図7に示したように、輪郭形状が複雑な細胞であっても、細胞内部の構造特徴により細胞密度の低い領域~高い領域の分布が算出され、ステップS33に進む。
Claims (21)
- 撮像装置により観察細胞が撮影された観察画像を取得し、
取得した前記観察画像から前記観察細胞の輪郭を抽出し、
輪郭が抽出された前記観察細胞に、細胞の外形形状をモデル化した細胞モデルを適応させて、
適応した前記細胞モデルを利用して前記観察細胞が移動すると予測される移動方向を導出することを特徴とする細胞観察画像の画像解析方法。 - 前記移動方向の導出は、前記適応した前記細胞モデルの形状特徴に基づいて行われることを特徴とする請求項1に記載の細胞観察画像の画像解析方法。
- 前記移動方向の導出は、前記適応した前記細胞モデルの重心位置と輪郭が抽出された前記観察細胞の重心位置のずれに基づいて行われることを特徴とする請求項1に記載の細胞観察画像の画像解析方法。
- 前記移動方向の導出は、前記適応した前記細胞モデルの輪郭形状と前記観察細胞の輪郭形状のずれに基づいて行われることを特徴とする請求項1に記載の細胞観察画像の画像解析方法。
- 撮像装置により観察細胞が撮影された観察画像を取得し、
取得した前記観察画像から前記観察細胞の輪郭を抽出し、
輪郭が抽出された前記観察細胞の輪郭形状に対する内部構造の偏りを算出して、
算出した前記内部構造の偏りに基づいて前記観察細胞が移動すると予測される移動方向を導出することを特徴とする細胞観察画像の画像解析方法。 - 前記内部構造の偏りが、前記観察細胞の細胞密度であることを特徴とする請求項5に記載の細胞観察画像の画像解析方法。
- 前記内部構造の偏りが、前記観察細胞のテクスチャ特徴であることを特徴とする請求項5に記載の細胞観察画像の画像解析方法。
- 撮像装置により観察細胞が撮影された観察画像を取得するステップと、
取得された前記観察画像から前記観察細胞の輪郭を抽出するステップと、
輪郭が抽出された前記観察細胞に、細胞の外形形状をモデル化した細胞モデルを適応させるステップと、
適応した前記細胞モデルを利用して前記観察細胞が移動すると予測される移動方向を導出するステップと、
導出された前記観察細胞の移動方向を外部に出力するステップと
を備えてなることを特徴とする細胞観察画像の画像処理プログラム。 - 前記移動方向の導出は、前記適応した前記細胞モデルの形状特徴に基づいて行われることを特徴とする請求項8に記載の細胞観察画像の画像処理プログラム。
- 前記移動方向の導出は、前記適応した前記細胞モデルの重心位置と輪郭が抽出された前記観察細胞の重心位置のずれに基づいて行われることを特徴とする請求項8に記載の細胞観察画像の画像処理プログラム。
- 前記移動方向の導出は、前記適応した前記細胞モデルの輪郭形状と前記観察細胞の輪郭形状のずれに基づいて行われることを特徴とする請求項8に記載の細胞観察画像の画像処理プログラム。
- 撮像装置により観察細胞が撮影された観察画像を取得するステップと、
取得した前記観察画像から前記観察細胞の輪郭を抽出するステップと、
輪郭が抽出された前記観察細胞の輪郭形状に対する内部構造の偏りを算出するステップと、
算出した前記内部構造の偏りに基づいて前記観察細胞が移動すると予測される移動方向を導出するステップと、
導出された前記観察細胞の移動方向を外部に出力するステップと
を備えてなることを特徴とする細胞観察画像の画像処理プログラム。 - 前記内部構造の偏りが、前記観察細胞の細胞密度であることを特徴とする請求項12に記載の細胞観察画像の画像処理プログラム。
- 前記内部構造の偏りが、前記観察細胞のテクスチャ特徴であることを特徴とする請求項12に記載の細胞観察画像の画像処理プログラム。
- 細胞を撮影する撮像装置と、
前記撮像装置により観察細胞が撮影された観察画像を取得して前記観察細胞が移動すると予測される移動方向を導出する画像解析部と、
前記画像解析部により導出された前記観察細胞の移動方向を外部に出力する出力部とを備え、
前記画像解析部が、前記観察画像から前記観察細胞の輪郭を抽出し、輪郭が抽出された前記観察細胞に細胞の外形形状をモデル化した細胞モデルを適応させて、適応した前記細胞モデルを利用して前記観察細胞が移動すると予測される移動方向を導出するように構成したことを特徴とする細胞観察の画像処理装置。 - 前記移動方向の導出は、前記適応した前記細胞モデルの形状特徴に基づいて行われることを特徴とする請求項15に記載の細胞観察画像の画像処理装置。
- 前記移動方向の導出は、前記適応した前記細胞モデルの重心位置と輪郭が抽出された前記観察細胞の重心位置のずれに基づいて行われることを特徴とする請求項15に記載の細胞観察画像の画像処理装置。
- 前記移動方向の導出は、前記適応した前記細胞モデルの輪郭形状と前記観察細胞の輪郭形状のずれに基づいて行われることを特徴とする請求項15に記載の細胞観察画像の画像処理装置。
- 細胞を撮影する撮像装置と、
前記撮像装置により観察細胞が撮影された観察画像を取得して前記観察細胞が移動すると予測される移動方向を導出する画像解析部と、
前記画像解析部により導出された前記観察細胞の移動方向を外部に出力する出力部とを備え、
前記画像解析部が、前記観察画像から前記観察細胞の輪郭を抽出し、輪郭が抽出された前記観察細胞の輪郭形状に対する内部構造の偏りを算出して、算出された前記内部構造の偏りに基づいて前記観察細胞が移動すると予測される移動方向を導出するように構成したことを特徴とする細胞観察の画像処理装置。 - 前記内部構造の偏りが、前記観察細胞の細胞密度であることを特徴とする請求項19に記載の細胞観察画像の画像処理装置。
- 前記内部構造の偏りが、前記観察細胞のテクスチャ特徴であることを特徴とする請求項19に記載の細胞観察画像の画像処理装置。
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EP (2) | EP2272971B1 (ja) |
JP (1) | JP2009229275A (ja) |
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Cited By (1)
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CN102712890A (zh) * | 2010-01-20 | 2012-10-03 | 株式会社尼康 | 细胞观察装置和细胞培养方法 |
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JP2010169823A (ja) * | 2009-01-21 | 2010-08-05 | Nikon Corp | 細胞観察画像の画像処理方法、画像処理プログラム及び画像処理装置 |
JP6078943B2 (ja) * | 2011-02-28 | 2017-02-15 | ソニー株式会社 | 画像処理装置および方法、並びに、プログラム |
CH705700B1 (it) * | 2011-10-28 | 2015-06-15 | Infergen Sa C O Bgtrust Company Sa | Dispositivo per coltivare, controllare e proteggere lo sviluppo di un embrione in vitro. |
JP2017090576A (ja) * | 2015-11-05 | 2017-05-25 | オリンパス株式会社 | 観察装置、及び、観察方法 |
CN107270828B (zh) * | 2017-07-05 | 2019-06-11 | 浙江科技学院 | 基于显微定量角度图像的细胞质心机械形变测量方法 |
JP7210355B2 (ja) * | 2019-03-27 | 2023-01-23 | 株式会社エビデント | 細胞観察システム、コロニー生成位置推定方法、推論モデル生成方法、およびプログラム |
DE102020107260A1 (de) * | 2020-03-17 | 2021-09-23 | Heinz Schade Gmbh | Inkubator |
EP4036212A1 (de) * | 2021-01-27 | 2022-08-03 | Eppendorf AG | Inkubator und verfahren |
EP4317410A1 (de) * | 2022-08-02 | 2024-02-07 | Eppendorf SE | Inkubator und verfahren |
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Also Published As
Publication number | Publication date |
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CN101868553A (zh) | 2010-10-20 |
EP2272971A1 (en) | 2011-01-12 |
EP3260550A1 (en) | 2017-12-27 |
JP2009229275A (ja) | 2009-10-08 |
EP2272971A4 (en) | 2011-09-14 |
EP2272971B1 (en) | 2017-08-09 |
US20110019923A1 (en) | 2011-01-27 |
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