JP5185151B2 - Microscope observation system - Google Patents

Description

  The present invention relates to a microscope observation system including a microscope.

  In biological tissue specimens, especially pathological specimens, block specimens obtained by organ excision and specimens obtained by needle biopsy are sliced into several μm, and are then widely observed with a microscope to obtain various findings. It has been broken. Above all, transmission observation using an optical microscope is one of the most popular observation methods because the equipment is relatively inexpensive and easy to handle and has been historically used for a long time. . In this case, the sliced biological specimen hardly absorbs and scatters light and is almost colorless and transparent. Therefore, it is general that the specimen is stained with a dye prior to observation.

  Various dyeing methods have been proposed, and the total number thereof reaches 100 or more. Particularly, regarding pathological specimens, hematoxylin-eosin staining using two of blue-purple hematoxylin and red eosin as pigments is proposed. (Hereinafter referred to as “HE staining”) is used as standard.

  In diagnosis of a pathological specimen (hereinafter referred to as “stained specimen”) stained with H & E, the pathologist comprehensively determines the shape and distribution of the tissue to be observed. In some cases, a method is used clinically in which a pathological specimen is subjected to special staining different from H & E staining, and the color of the tissue to be observed is changed and visually enhanced. This special staining is used, for example, when observing a tissue that is difficult to confirm by H & E staining, or when it is difficult to visually recognize the tissue shape to be observed due to the progression of cancer. However, when special staining is performed, there is a problem in that costs and work processes at the clinical site are increased. Further, depending on the dyeing state, the dyeing may be insufficient and the visibility may not be improved, and it may be difficult to identify the tissue to be observed.

  For this reason, an attempt to identify a tissue by image processing without actually performing special staining has been proposed (see, for example, Patent Document 1). In this patent document 1, an observation image of a pathological specimen by a microscope optical system is taken as a multiband image, and a spectrum (spectral transmittance) of the pathological specimen is estimated based on the obtained multiband image. Then, the pigment amount of the pathological specimen is estimated from the estimated spectral transmittance, the distribution of the nucleus and cytoplasm is obtained from the pigment amount distribution, and the cancer site is estimated based on these distribution ratios.

  By the way, a multiband image can be obtained by using a multiband camera and capturing a stained specimen while switching bands. Here, when switching the band, it is necessary to change the setting of the multiband camera in accordance with the band to be switched. For example, Patent Document 2 discloses a technique in which parameters necessary for setting a multiband camera are stored in advance for each band, and the parameters corresponding to switching between bands are read out to automatically perform necessary settings. ing.

JP 2004-286666 A Japanese Patent No. 4112469

  If the technique of Patent Document 1 is applied, it is affected by the tissue that is difficult to discriminate from the difference in color, the staining state, etc., based on the spectral characteristics of the stained sample estimated by multiband imaging of the stained sample. Therefore, it is possible to discriminate tissues that are difficult to visually recognize. However, in practice, the characteristic spectrum band and its bandwidth differ depending on the attribute values that are individually determined for each type of stained specimen, such as the type of staining, the collected organ, the tissue to be observed, and the stained facility. For this reason, it is necessary to set the operating environment (system environment) of the microscope and the multiband camera according to the attribute value of the stained specimen to be observed, and depending on the combination of a plurality of attribute values. It is difficult to set parameters in advance.

  The present invention has been made in view of the above, and an object thereof is to provide a microscope observation system capable of automatically setting an optimum system environment for acquiring characteristics of a specimen to be observed. .

  In order to solve the above-described problems and achieve the object, a microscope observation system according to an aspect of the present invention includes an observation unit that observes a sample using a microscope, and an observation system control unit that controls the operation of the observation unit And a characteristic data storage unit that stores, for each attribute value, characteristic data determined according to an attribute value indicating the attribute of the sample, and the observation system control unit determines the attribute value of the sample to be observed. A specimen attribute designating unit for designating, and a characteristic data selecting unit for selecting at least one characteristic data corresponding to the attribute value designated by the sample attribute designating unit from the characteristic data stored in the characteristic data storage unit; A characteristic data analyzing unit that analyzes the characteristic data selected by the characteristic data selecting unit, and observing the specimen to be observed based on an analysis result by the characteristic data analyzing unit Wherein the observation unit system environment setting unit for setting the system parameters for setting the operation environment, characterized in that it comprises a.

  According to the microscope observation system according to this aspect, the characteristic data determined according to the attribute value indicating the specimen attribute is stored for each attribute value, and the characteristic data according to the attribute value of the specimen to be observed is selected. can do. Then, based on the analysis result of the selected characteristic data, it is possible to set a system parameter for setting the operating environment of the observation unit when observing the specimen to be observed. Therefore, it is possible to automatically set an optimal system environment for acquiring the characteristics of the specimen to be observed.

  According to the present invention, it is possible to automatically set an optimal system environment for acquiring the characteristics of a specimen to be observed.

  DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. In the present embodiment, a microscope observation system will be described in which a stained specimen, which is a pathological specimen stained with H & E, is used as a subject, and this stained specimen is observed by multiband imaging. Note that the present invention is not limited to the embodiments. Moreover, in description of drawing, the same code | symbol is attached | subjected and shown to the same part.

(Embodiment 1)
FIG. 1 is a schematic diagram illustrating the overall configuration of the microscope observation system 1 according to the first embodiment. FIG. 2 is a block diagram showing a functional configuration of the microscope observation system 1. The microscope observation system 1 according to the first embodiment includes an observation unit 3, an observation system control unit 5, and a characteristic data storage unit 7, which are connected so as to be able to exchange data.

  The observation unit 3 includes a stained sample observation unit 31 for observing a stained sample, and a stained sample image capturing unit 33 for capturing a stained sample image.

  The stained specimen observation unit 31 includes a microscope capable of observing the stained specimen through transmission, and includes a light source and an objective lens that emit illumination light, and a stained specimen to be observed (hereinafter, the stained specimen to be observed is referred to as an “observed stained specimen”). .) Is mounted on the motorized stage that moves in the optical axis direction of the objective lens and in the plane perpendicular to the optical axis direction, the illumination optical system for illuminating the observation stained specimen on the motorized stage, the objective lens, and the observation An observation optical system for forming an observation image of the stained specimen is provided. The stained specimen observation unit 31 irradiates the observation stained specimen with illumination light from a light source by the illumination optical system, and forms an observation image of the observation stained specimen by the observation optical system in cooperation with the objective lens.

  The stained specimen image capturing unit 33 includes a multiband camera that captures an observation image of the observed stained specimen in a multiband manner. For example, a tunable filter, a two-dimensional CCD camera, or a filter that adjusts the wavelength of light transmitted through the tunable filter. It consists of a controller, a camera controller for controlling a two-dimensional CCD camera, and the like. The stained specimen image imaging unit 33 projects an observation image of the observed stained specimen observed by the stained specimen observation unit 31 onto an imaging element of a two-dimensional CCD camera via a tunable filter and captures it as a stained specimen image. The tunable filter is a filter capable of electrically adjusting the wavelength of transmitted light. In the first embodiment, the tunable filter has a wavelength of an arbitrary width of 1 [nm] or more (hereinafter referred to as “selected wavelength width”). Use a band selectable one. For example, commercially available products such as a liquid crystal tunable filter “VariSpec (VariSpec)” manufactured by Cambridge Research and Instrumentation may be used as appropriate. The stained sample image capturing unit 33 obtains a stained sample image as a multiband image. Here, the pixel value of the stained specimen image corresponds to the intensity of light in the wavelength band arbitrarily selected by the tunable filter, and the pixel value of the wavelength band selected for each point of the observation stained specimen is obtained. Note that each point on the observation stained specimen refers to each point on the observation stained specimen corresponding to each pixel of the projected image sensor. Assume that it corresponds to the pixel position.

  As described above, the configuration using the tunable filter is exemplified as the configuration of the stained specimen image capturing unit 33. However, the configuration is not limited to this, and it is only necessary to obtain the light intensity information at each point of the observation stained specimen. For example, an imaging method disclosed in Japanese Patent Laid-Open No. 7-120324 is used, and an observation stained specimen is multibanded by a surface sequential method while switching a predetermined number (for example, 16) of bandpass filters by rotating them with a filter wheel. It is good also as a structure which images.

  The observation system control unit 5 is for a doctor or the like to observe and diagnose the stained sample image captured by the stained sample image capturing unit 33 of the observation unit 3, and is realized by a general-purpose computer such as a workstation or a personal computer. Is done. The observation system control unit 5 gives an operation instruction to the stained specimen observation unit 31 and the stained specimen image capturing unit 33 constituting the observation unit 3, processes the stained specimen image input from the stained specimen image capturing unit 33, and displays it. To display.

  As shown in FIG. 2, the observation system control unit 5 includes an operation unit 51, a display unit 52, a processing unit 54, and a storage unit 55.

  The operation unit 51 is realized by, for example, a keyboard, a mouse, a touch panel, various switches, and the like, and outputs an operation signal corresponding to the operation input to the processing unit 54. The display unit 52 is realized by a flat panel display such as an LCD or an EL display, or a display device such as a CRT display, and displays various screens according to display signals input from the processing unit 54.

  The processing unit 54 is realized by hardware such as a CPU. The processing unit 54 includes an operation signal input from the operation unit 51, image data of a stained sample image input from the stained sample image capturing unit 33 of the observation unit 3, a program and data stored in the storage unit 55, and the like. In addition, the microscopic observation system is configured such that instructions and data transfer to the respective units constituting the observation system control unit 5 are performed, or various operation instructions are given to the stained specimen observation unit 31 and the stained specimen image capturing unit 33 of the observation unit 3. 1 Overall control of the overall operation.

  The processing unit 54 includes a stained specimen attribute designation unit 541, a characteristic data selection unit 542, a characteristic data analysis unit 543, a system environment setting unit 544, and a target object extraction unit 545.

  The stained specimen attribute designating unit 541 designates an attribute value indicating the attribute of the observed stained specimen in accordance with a user operation. Here, the attribute of the stained specimen (hereinafter, the attribute of the stained specimen is referred to as “stained specimen attribute”) is composed of the four attribute items of the staining type, the organ, the target tissue, and the facility. The attribute designation unit 541 designates attribute values of these four attribute items related to the observation stained specimen according to a user operation. In the first embodiment, the user designates the observation magnification of the microscope (stained specimen observation unit 31) when observing the observed stained specimen together with designation of the stained specimen attribute of the observed stained specimen. It has become.

  The characteristic data selection unit 542 selects one or more characteristic data from the characteristic data stored in the characteristic data storage unit 7 based on the stained specimen attribute designated by the stained specimen attribute designation unit 541.

  The characteristic data analysis unit 543 determines a characteristic wavelength of the observation stained specimen, more specifically, a target tissue as a characteristic wavelength, based on one or more characteristic data selected by the characteristic data selection unit 542.

  The system environment setting unit 544 is for setting the operating environment (system environment) of the observation unit 3 so that the sensitivity is improved with respect to a wavelength band having a predetermined width including the characteristic wavelength determined by the characteristic data analysis unit 543. Set system parameters. For example, the system environment setting unit 544 sets an observation parameter for setting the operating environment of the stained specimen observation unit 31 and an imaging parameter for setting the operating environment of the stained specimen image capturing unit 33 as system parameters.

  The target object extraction unit 545 performs image processing on the stained sample image captured by the stained sample image capturing unit 33 of the observation unit 3, and extracts a region in which the target tissue is reflected from the stained sample image.

  The storage unit 55 is realized by various IC memories such as ROM and RAM such as flash memory that can be updated and stored, an information storage medium such as a built-in or connected data communication terminal, a CD-ROM, and a reading device thereof. It is. The storage unit 55 stores a program for operating the observation system control unit 5 and realizing various functions provided in the observation system control unit 5, data used during the execution of the program, and the like. . In addition, the storage unit 55 stores an observation system control program 551. The observation system control program 551 is a program for realizing a process of setting a system parameter based on the stained specimen attribute of the observation stained specimen, controlling the operation of the observation unit 3, and acquiring a stained specimen image.

  The characteristic data storage unit 7 stores characteristic data corresponding to the attribute value of each attribute item of the stained specimen attribute. The characteristic data storage unit 7 is realized by, for example, a database device connected to the observation system control unit 5 via a network, and is installed in a separate place away from the observation system control unit 5 to store characteristic data. Manage characteristic data. The characteristic data may be stored in the storage unit 55 of the observation system control unit 5.

  3 to 5 are diagrams for explaining examples of the data structure of the characteristic data stored in the characteristic data storage unit 7. Here, FIG. 3 shows a list of characteristic data stored in the characteristic data storage unit 7 in association with the staining type that is one of the attribute items. FIG. 4 shows a list of characteristic data stored in the characteristic data storage unit 7 in association with the target tissue that is one of the attribute items. FIG. 5 shows a list of characteristic data stored in the characteristic data storage unit 7 in association with a facility that is one of the attribute items. The association of the characteristic data for each attribute item shown in FIGS. 3 to 5 is managed using, for example, a known database management tool. However, the data structure of the characteristic data is not limited to this, and any structure may be used as long as the characteristic data corresponding to the attribute value can be acquired by specifying the attribute value of each attribute item.

  Specifically, as shown in FIG. 3, in the characteristic data storage unit 7, as characteristic data on the staining type, for each attribute value, a facility that is one of the attribute items and an observation magnification that is an observation parameter And the measurement date and the observation spectral characteristic are stored in association with each other. The observation spectral characteristics (data set A-01 to -06,...) Associated with the staining type are spectral characteristic data (spectral data) measured in advance for the staining dye of the corresponding staining type, and the corresponding medical characteristics. The spectral characteristic data measured at the corresponding measurement date at the facility at the corresponding observation magnification is stored. Here, the observation spectral characteristic represents the characteristic of the substance with respect to light, and for example, spectral characteristic values such as spectral transmittance, absorbance, spectral reflectance, and the like can be used.

  In addition, as shown in FIG. 4, the characteristic data storage unit 7 stores, as characteristic data about the target tissue, for each attribute value, the staining type, facility, and organ as attribute items, and the focal position as an observation parameter. The aperture, the measurement date, and the observation spectral characteristics are stored in association with each other. The observation spectral characteristics (data sets B-01 to B-09,...) Associated with the target tissue of interest are spectral characteristics measured in advance with respect to the target tissue of the corresponding organ. The spectral characteristic data measured at the corresponding measurement date is stored at the corresponding focal position and aperture. In FIG. 4, elastic fibers and collagen fibers are listed as examples of the target tissue. However, in addition to this, the spectral characteristics of the target tissue that the doctor, such as cytoplasm and nucleus, pays attention during observation / diagnosis are shown. It is measured in advance and stored in the characteristic data storage unit 7.

  Further, as shown in FIG. 5, in the characteristic data storage unit 7, as characteristic data about the facility, for each attribute value, a staining type and an organ that are attribute items, an observation magnification that is an observation parameter, and a measurement date Are stored in association with the system spectral characteristics of the white image signal value, the illumination spectral characteristic, and the camera spectral characteristic. The system spectral characteristic associated with this facility stores spectral characteristic data for the system measured at the corresponding facility. Specifically, the white image signal values (data sets C-01 to C-05,...) Are image signal values obtained by imaging an area where no tissue exists, and correspond with corresponding observation magnifications. The image signal value measured on the measurement date is stored. The illumination light spectral characteristics (data sets C-11 to C-15,...) Are illuminations of the stained specimen observation unit 31 measured using a spectrometer or the like at a corresponding observation magnification at a corresponding observation magnification. The spectral characteristics of Further, the camera spectral characteristics (data sets C-21 to C-25,...) Are the cameras (two-dimensional CCD camera) of the stained specimen image capturing unit 33 measured at the corresponding observation magnification and the corresponding measurement date. The spectral characteristics of

  In addition, it corresponds to the case where special imaging is performed for a fiber region or a phase object, for example, spectral characteristics are measured under different observation parameters such as an NA value, a defocus amount, and a light amount, and the characteristic data is associated with the measurement conditions. It may be stored in the storage unit 7. In addition, depending on the facility, the staining process and reagents used for staining may differ. Corresponding to such a case, the spectral characteristics may be measured under conditions corresponding to each facility, and stored in the characteristic data storage unit 7 in association with the measurement conditions.

  FIG. 6 is a flowchart illustrating a processing procedure performed by the observation system control unit 5 according to the first embodiment. Note that the processing described here is realized by the operation of each unit of the observation system control unit 5 in accordance with the observation system control program 551 stored in the storage unit 55.

  First, the stained specimen attribute designating unit 541 performs a process of displaying a stained specimen attribute designation screen on the display unit 52 and notifying a designation of designation of a stained specimen attribute, and the stained specimen attribute and observation magnification by the user via the operation unit 51. Is received (step a1).

  FIG. 7 is a diagram illustrating an example of a stained specimen attribute designation screen according to the first embodiment. In the stained specimen attribute designation screen shown in FIG. 7, spin boxes B11 to B14 for designating attribute values of four attribute items of a staining type, an organ, a target tissue and a facility, and a spin box for designating an observation magnification B15, an OK button BTN11 for confirming the operation in each of these spin boxes, a cancel button BTN12 for canceling the operation, and the like are arranged. Each of the spin boxes B11 to B14 presents a list of attribute values that can be specified for the corresponding attribute item as an option and accepts a specifying operation. The attribute value of each attribute item stored in the characteristic data storage unit 7 is Presented as an option. For example, in the spin box B11 for designating the staining type, the attribute values of the staining types such as HE staining, VB staining, orcein staining, MT staining, and DAB staining exemplified in FIGS. Each option of “Others” to be specified when not included is presented. Similarly, the spin values B12 to B14 of the organ, the target tissue, and the facility are also presented with attribute values stored in the characteristic data storage unit 7 and options of “others”. Further, the spin box B15 presents a value that can be specified as the value of the observation magnification as an option and prompts the selection, and the value of the observation magnification stored in the characteristic data storage unit 7 is presented as an option.

  On this stained specimen attribute designation screen, the user designates the type of staining applied to the observed stained specimen as the staining type. Further, the user designates the organ from which the observed stained specimen is collected as the organ. In addition, the user designates a target tissue (attention target tissue) to be noted when observing and diagnosing the observation stained specimen. In addition, the user designates, for example, a medical facility where the observed stained specimen is collected as a facility. The user also specifies the observation magnification together with these four stained specimen attributes.

  Further, a memo column M11 is arranged on the stained specimen attribute designation screen, and the user can freely enter items such as the date when the stained specimen is created and the date when the stained specimen is observed and diagnosed. ing.

  If the attribute value of the stained specimen attribute is designated, then, as shown in FIG. 6, the characteristic data selection unit 542 refers to the characteristic data storage unit 7 and corresponds to the designated attribute value of the stained specimen attribute. One or more characteristic data is selected (step a3). Subsequently, the characteristic data analysis unit 543 first performs a characteristic wavelength candidate determination process based on the characteristic data selected in step a3 (step a4), and subsequently performs a characteristic wavelength determination process (step a5). The characteristic wavelength of the target tissue is determined. Subsequently, the system environment setting unit 544 sets system parameters (observation parameters and imaging parameters) based on the characteristic wavelength determined in step a5 (step a7). The system environment setting unit 544 outputs the set system parameters to the stained sample observation unit 31 and the stained sample image capturing unit 33 to instruct operation of each unit. As a result, the observation unit 3 operates according to the system parameters set by the system environment setting unit 544, and obtains a stained sample image by performing multiband imaging of the observation image of the stained sample (step a9). The attention target extraction unit 545 performs attention target tissue extraction processing, and performs image processing on the stained specimen image to extract a region in which the attention target tissue appears in the stained specimen image (step a11). As the extraction method, a known method can be applied.

  Here, on the stained specimen attribute designation screen shown in FIG. 7, the user selects “HE staining” as the staining type, “kidney” as the organ, “elastic fiber” as the target tissue, “A hospital” as the facility, and “20” as the observation magnification. Taking the case of “double” as an example, each processing of step a3 to step a11 will be described in detail.

  First, step a3 in FIG. 6 will be described. In the case of the exemplified conditions, the characteristic data selection unit 542 refers to the characteristic data storage unit 7, and among the characteristic data for the staining type shown in FIG. 3, the staining type is “HE staining” and the facility is “A Records R11 and R12 having “hospital” and an observation magnification of “20 times” are selected, and corresponding observation spectral characteristic data sets A-01 and A-03 are acquired. In addition, the characteristic data selection unit 542 selects the “target elastic tissue”, the staining type “HE staining”, and the facility “A hospital” from the characteristic data on the target tissue shown in FIG. And records R21 to R23 whose organ is “kidney” are selected, and corresponding observation spectral characteristic data sets B-01, B-02, and B-03 are acquired. In addition, the characteristic data selection unit 542 selects, from the characteristic data for the facility shown in FIG. 5, the facility is “Hospital A”, the staining type is “HE staining”, the organ is “kidney”, and the observation magnification is The record R31 that is “20 times” is selected, and the corresponding system spectral characteristic data sets C-01, C-11, and C-21 are acquired. Data sets A-01, A-03, B-01, B-02, B-03 of observation spectral characteristics and system spectral characteristics (white image signal values, illumination spectral characteristics and camera spectral characteristics) C- 01, C-11, and C-21 are stored in the storage unit 55 together with the attribute value and observation magnification value of each attribute item designated on the stained specimen attribute designation screen.

  Next, the characteristic wavelength candidate determination process in step a4 in FIG. 6 will be described. In this characteristic wavelength candidate determination process, the characteristic data analysis unit 543 performs the characteristic wavelength candidate Cλ (i) (i = 1) of the target tissue based on the observation spectral characteristic (data set) acquired as described above. , 2, 3,..., N) are determined. In the first embodiment, for example, based on the change rate between wavelengths of observation spectral characteristics, a wavelength at which the change rate between wavelengths exceeds a predetermined threshold is set as a feature wavelength candidate. FIG. 8 is a flowchart showing a detailed processing procedure of the characteristic wavelength candidate determination processing.

As shown in FIG. 8, first, the characteristic data analysis unit 543 reads and acquires the observation spectral characteristic (data set) of the characteristic data selected in step a3 in FIG. 6 (step b11). Subsequently, the characteristic data analysis unit 543 calculates an inter-wavelength change rate r (λ) of the acquired observation spectral characteristic (step b13). The rate of change r (λ) between wavelengths is expressed by the following equation (1), where A (λ) is the observation spectral characteristic at the wavelength λ, and α [nm] is the wavelength interval. Through this processing, the inter-wavelength change rate r (λ) of each observation spectral characteristic of each acquired data set A-01, A-03, B-01, B-02, B-03 is calculated. The

Subsequently, the characteristic data analysis unit 543 calculates a threshold value Th for determining a characteristic wavelength candidate (step b15). First, the characteristic data analysis unit 543 calculates an inter-wavelength change rate average E and a standard deviation std of observation spectral characteristics. Here, assuming that the minimum wavelength is λ _MIN , the number of wavelengths is S _num , and the average of observation spectral characteristics is ave, the average change rate between wavelengths E is expressed by the following equation (2), and the standard deviation std of the observation spectral characteristics is It is represented by Formula (3).

However, the number of wavelengths S _num is expressed by the following equation (4). λ _MAX represents the maximum wavelength.

And the characteristic data analysis part 543 calculates threshold value Th according to following Formula (5). k is a coefficient set arbitrarily.
Th = E + k × std (5)

  Subsequently, the characteristic data analysis unit 543 sequentially performs threshold processing on the change rate r (λ) between the wavelengths of the observation spectral characteristics calculated in step b13 using the threshold Th calculated in step b15. Then, when the inter-wavelength change rate r (λ) is larger than the threshold Th (step b17: Yes), the characteristic data analysis unit 543 calculates the inter-wavelength change rate r (λ) larger than the threshold Th 2 Two wavelengths (λ + α, λ) are set as feature wavelength candidates Cλ (i) (step b19). If the threshold processing is performed for all the inter-wavelength change rates r (λ), the process returns to step a4 in FIG. By the processing here, the threshold value among the inter-wavelength change rates r (λ) calculated for the observation spectral characteristics of the data sets A-01, A-03, B-01, B-02, and B-03. Two wavelengths λ + α and λ for which an inter-wavelength change rate r (λ) that is equal to or greater than Th are calculated are determined as characteristic wavelength candidates Cλ (i).

  Next, the characteristic wavelength determination process in step a5 in FIG. 6 will be described. In this characteristic wavelength determination process, the characteristic data analysis unit 543 determines a characteristic wavelength from the characteristic wavelength candidates Cλ (i) determined in step a4. Which tissue is preferentially stained with the staining dye (whether it is easily stained) is determined physicochemically. Therefore, in accordance with the relationship between the target tissue and the staining pigment (specifically, a staining pigment that preferentially stains the target tissue and a staining pigment that does not stain the target tissue), the characteristic wavelength Kλ (i) (i = 1, 2, 3,..., N) are determined. FIG. 9 is a flowchart showing a detailed processing procedure of the characteristic wavelength determination processing.

Here, the observation spectral characteristics (here, data sets A-01 and A-03) of the staining types indicate the characteristics of the corresponding staining dyes (here, dyes H and E). The feature wavelength candidate obtained from the observation spectral characteristic of the staining dye that preferentially stains the target tissue is D ₁ λ (k) (k = 1, 2, 3,..., M ₁ ), and the target tissue of interest Let D ₂ λ (j) (j = 1, 2, 3,..., M ₂ ) be a characteristic wavelength candidate obtained from a staining dye that does not stain. For example, the elastic fibers exemplified as the target tissue in Embodiment 1 are preferentially stained with the dye E. For this reason, the characteristic wavelength candidate obtained from the observation spectral characteristic (data set A-03) corresponding to the dye E is defined as D ₁ λ (k). In addition, since the dye H is a dye that does not stain elastic fibers, a characteristic wavelength candidate obtained from the observation spectral characteristics (data set A-01) corresponding to the dye H is defined as D ₂ λ (k).

As shown in FIG. 9, first, the characteristic data analysis unit 543 converts the characteristic wavelength candidate Cλ (i) (i = 1, 2, 3,..., N) into the characteristic wavelength Kλ (i) (i = 1, 2, 3, ..., n) (step b21). Subsequently, the characteristic data analysis unit 543 performs Cλ (i) (i = 1, 2, 3,..., N) and D ₂ λ (j) (j = 1, 2, 3,..., M ₂ ) to determine whether each wavelength of Cλ (i) matches any wavelength of D ₂ λ (j). If there is a wavelength that matches the wavelength of D ₂ λ (j) among the wavelengths of Cλ (i) (step b23: Yes), the characteristic wavelength Kλ (i) (i = 1, 2, 3,. .., N), the wavelength of Cλ (i) determined to match is excluded (step b25).

Subsequently, the characteristic data analysis unit 543 performs Cλ (i) (i = 1, 2, 3,..., N) and D ₁ λ (k) (k = 1, 2, 3,..., M _1). ) To determine whether each wavelength of D ₁ λ (k) matches any wavelength of Cλ (i). If there is a wavelength that does not match any wavelength of Cλ (i) among the wavelengths of D ₁ λ (k) (step b27: Yes), the characteristic wavelength Kλ (i) (i = 1, 2, 3). ,..., N) is added with the wavelength of D ₁ λ (k) determined not to match (step b29). Then, the characteristic data analysis unit 543 finally determines the wavelength still registered in Kλ (i) (i = 1, 2, 3,..., N) as the characteristic wavelength. Thereafter, the process returns to step a5 in FIG. 6 and proceeds to step a7.

  In addition, after the characteristic wavelength is determined, the characteristic wavelength confirmation screen may be displayed on the display unit 52, and the determined characteristic wavelength may be presented to the user. FIG. 10 is a diagram illustrating an example of a characteristic wavelength confirmation screen. As shown in FIG. 10, for example, on the characteristic wavelength confirmation screen, the observation spectral characteristic of the characteristic data selected according to the stained specimen attribute of the observation stained specimen is graphed and displayed, and the determined characteristic wavelength is indicated by a broken line. Is displayed. Here, FIG. 10 is a graph of the observed spectral characteristics (for example, data set B-02) of the characteristic data in which the target tissue is selected as “elastic fiber”, and the spectral transmittance is used as the observed spectral characteristics. The case is illustrated.

  The characteristic wavelength determination method described here is merely an example, and the present invention is not limited to this. For example, a wavelength at which the rate of change between wavelengths is maximized may be set as the characteristic wavelength without performing threshold processing using the threshold Th. Alternatively, a principal component analysis of observation spectral characteristics of each data set may be performed in advance, and a wavelength with a high contribution may be used as a characteristic wavelength using the principal component analysis result.

  Alternatively, the characteristic wavelength may be determined by comparing observation spectral characteristic data sets acquired by the characteristic data selection unit 542. For example, as described above, a plurality of data sets B-01, B-02, and B-03 having different observation parameters such as a focus position and a diaphragm of the microscope (observation unit 3) were acquired as observation spectral characteristics data sets. In such a case, the feature wavelength may be determined by comparing these data sets B-01, B-02, and B-03. For example, the following processing is performed for each combination of acquired data sets (in this case, three combinations of B-01 and B-02, B-01 and B-03, and B-02 and B-03). That is, the difference in observation spectral characteristics is calculated for each wavelength for each combination. Then, a wavelength at which the calculated difference is larger than a predetermined threshold set in advance is determined as the characteristic wavelength.

  Next, step a7 in FIG. 6 will be described. First, the system environment setting unit 544 sets an observation parameter and an imaging parameter (system parameter) based on the characteristic wavelength determined as described above.

  Here, the imaging parameter is a value related to the operation of the multiband camera, and the system environment setting unit 544 notifies the stained specimen image capturing unit 33 of the set imaging parameter value and instructs the stained specimen image capturing unit 33 to operate. I do. The stained specimen image capturing unit 33 responds to the operation instruction from the system environment setting unit 544 and, for example, sets the gain, sets the exposure time, and selects the wavelength band (selected wavelength width) by the tunable filter according to the notified imaging parameter. ) To drive the multiband camera.

  In the first embodiment, the system environment setting unit 544 sets a selected wavelength band (selected wavelength width) of the tunable filter as one of the imaging parameters. For example, the selected wavelength width in the wavelength band of 5 [nm] before and after the characteristic wavelength is set to 1 [nm] which is the minimum wavelength width that can be selected by the tunable filter. Further, the selection wavelength width in a wavelength band other than the wavelength band of 5 [nm] before and after the characteristic wavelength is set to an initial value (for example, 25 [nm]). The stained specimen image capturing unit 33 sequentially selects wavelength bands to be selected by the tunable filter according to the selected wavelength width for each wavelength band set here, and captures a stained specimen image for each selected wavelength band.

  The system environment setting unit 544 sets the exposure time as the second imaging parameter. For example, the system environment setting unit 544 uses a data set of white image signal values (here, data set C-01) selected by the characteristic data selection unit 542 so that the maximum value of the white image signal values has a predetermined luminance value. The exposure time is adjusted to an exposure time in a wavelength band other than the wavelength band of 5 [nm] before and after the characteristic wavelength. On the other hand, for the exposure time in the wavelength band of 5 nm before and after the characteristic wavelength, the system environment setting unit 544 first gives various operation instructions to the stained specimen observation unit 31 and the stained specimen image capturing unit 33 of the observation unit 3, A white image signal value is acquired at a specified observation magnification. Then, the system environment setting unit 544 calculates the exposure time for each measurement wavelength using the acquired white image signal value. According to this, the exposure time can be set in the vicinity of the characteristic wavelength according to the environment at the time of observation (when the stained specimen image is captured).

  Here, the selected wavelength band (selected wavelength width) and exposure time of the tunable filter and the exposure time are set as the imaging parameters. However, other values related to the settings are also appropriately set as necessary. Can be set as

  On the other hand, the observation parameter is a value related to the operation of the microscope, and the system environment setting unit 544 notifies the stained sample observation unit 31 of the set observation parameter value and gives an operation instruction to the stained sample observation unit 31. In response to the operation instruction from the system environment setting unit 544, the stained specimen observation unit 31 switches light source dimming control according to, for example, switching of the observation magnification of the objective lens or the switched observation magnification according to the notified observation parameter, etc. Adjustment of various parts of the microscope accompanying observation of the observation stained specimen is performed, such as switching of various optical elements and movement of the electric stage.

  In the first embodiment, the system environment setting unit 544 sets each value as the observation magnification, the focal position, and the aperture of the microscope as the observation parameters.

  For the observation magnification, the designated observation magnification is set. For the focal position and the stop, values corresponding to the observation spectral characteristics (data set) used when acquiring the determined characteristic wavelength (wavelength of Kλ (i)) are read from the characteristic data storage unit 7 and set. For example, if the characteristic wavelength Kλ (i) is determined based on the rate of change between wavelengths calculated from the observation spectral characteristics of the data set B-02, the record of FIG. 4 corresponding to the data set B-02. The focal position “± zero” and the aperture “magnification × 0.6” shown in R22 are set as observation parameters. The stained specimen observation unit 31 uses the observation parameters set here, and the focal position and aperture when the stained specimen image imaging unit 33 captures the stained specimen image in a wavelength band including the characteristic wavelength Kλ (i). Set the value of.

  Note that, in the case where a plurality of observation spectral characteristics (data sets) are acquired, the characteristic wavelength Kλ (i) is based on the rate of change between wavelengths calculated based on the observation spectral characteristics of different data sets. When determined (for example, the characteristic wavelength Kλ (i) overlaps based on the inter-wavelength change rates calculated individually from the observation spectral characteristics of the data sets B-01, B-02, and B-03. The observed spectral characteristics (data set) obtained the inter-wavelength change rate having the largest value among the inter-wavelength change rates calculated from the observed spectral characteristics at the characteristic wavelength Kλ (i). Is set as an observation parameter.

  Further, as described above as a modification, when the wavelength having the maximum rate of change between wavelengths is set as the characteristic wavelength, the focal point position and the aperture corresponding to the data set referred to in calculating the rate of change between wavelengths are set as characteristic data Read from the storage unit 7 and set. In addition, when the principal component analysis result of the observation spectral characteristics of each data set is used and the wavelength having the highest contribution is set as the characteristic wavelength, the data set of the principal component analysis result having the wavelength having the highest contribution is obtained. The corresponding focal position and aperture are read from the characteristic data storage unit 7 and set.

  In addition, if a plurality of observation spectral characteristics (data sets) are acquired, and if the characteristic wavelength is determined by calculating the difference of the observation spectral characteristics for each wavelength for each combination of each data set, The focal position and aperture corresponding to each data set referred to when the characteristic wavelength is determined are read out, and two types of observation parameters are set together with the designated observation magnification. For example, when the difference between the observation spectral characteristics of the data sets B-01 and B-02 at a certain wavelength is large and the wavelength is determined as the characteristic wavelength, “record R21 in FIG. 4 corresponding to the data set B-01” The indicated focal position “− (minus)” and aperture “zero” are read out, and the first observation parameter is set. Further, the focus position “± zero” and the aperture “magnification × 0.6” shown in the record R22 of FIG. 4 corresponding to the data set B-02 are read, and the second observation parameter is set.

  In this example, the observation magnification of the microscope, the focus position, and the diaphragm are set as observation parameters. However, values related to other settings can be set as appropriate observation parameters as necessary. .

  The system parameters (observation parameters and imaging parameters) set as described above are stored in the storage unit 55 as a system setting file associated with the stained specimen attribute. By storing the system parameters set in this way as a system setting file, when the same combination of stained specimen attributes and observation magnification is designated thereafter, the system parameters are set by reading the system setting file. It becomes possible.

  In step a9 of FIG. 6, the system environment setting unit 544 sequentially outputs the exposure time in the corresponding wavelength band together with the selected wavelength width to the stained specimen image capturing unit 33, and sets the observation magnification and focus position set as observation parameters. Then, each value of the aperture is output to the stained specimen observation unit 31, and a stained specimen image is acquired for each selected wavelength width. The acquired image data of the stained specimen image is stored in the storage unit 55. At this time, as described above as a modified example, when two types of observation parameters, the first observation parameter and the second observation parameter, are set, the system environment setting unit 544 sets the first observation parameter and the first observation parameter. The two observation parameters are sequentially output to the stained specimen observation unit 31, and the stained specimen images are acquired twice with different observation parameters. That is, a stained specimen is observed using the observation parameter as the first parameter, the first stained specimen image obtained by multiband imaging, and the stained specimen is observed using the observation parameter as the second parameter, and the second staining obtained by multiband imaging. A sample image is obtained.

  Next, the target object extraction process in step a11 in FIG. 6 will be described. FIG. 11 is a flowchart showing a detailed processing procedure of the target extraction process. As shown in FIG. 11, in the target extraction process, the target target extraction unit 545 first changes based on a characteristic wavelength and a stained specimen image (spectral image) at a wavelength of 1 [nm] before and after the characteristic wavelength. A rate spectral image is created (step c1). The “spectral image” refers to a stained sample image at a specific wavelength among the stained sample images.

  That is, first, the target object extraction unit 545 creates a change rate spectral image from the spectral image of the characteristic wavelength ω [nm] and the spectral image of the wavelength ω-1 [nm]. Specifically, the spectral transmittance at each point of the stained specimen corresponding to each pixel is calculated based on the image signal value and the white image signal value of the spectral image at the characteristic wavelength ω [nm] and the wavelength ω-1 [nm]. Processing to calculate is performed. Then, using the spectral transmittance calculated for each pixel, in the same manner as the calculation procedure by the characteristic data analysis unit 543, according to the above-described equation (1), the change rate r (the inter-wavelength change rate r (between spectral images). λ) (that is, the absolute value of the difference in spectral transmittance between the characteristic wavelengths ω [nm] and ω-1 [nm]) is calculated for each pixel. Then, the pixel value of the pixel having the maximum inter-wavelength change rate r (λ) is “255”, and the pixel value of the pixel having the inter-wavelength change rate r (λ) is zero is set to “0 (zero)”. A pixel value corresponding to the size of the rate r (λ) is assigned to each pixel, and a change rate spectral image is created as a grayscale image. Similarly, a change rate spectral image is created from the spectral image of the characteristic wavelength ω [nm] and the spectral image of the wavelength ω + 1 [nm].

  Then, the target object extraction unit 545 combines the created two change rate spectral images to create a change rate combined spectral image (step c3). FIG. 12 is a diagram illustrating an example of the change rate combined spectral image. This change rate composite spectral image is obtained as an image in which pixels having a large inter-wavelength change rate r (λ) are emphasized. Here, when a plurality of wavelengths are determined as the characteristic wavelengths by the characteristic wavelength determination processing of FIG. 9, the processing of step c1 to step c3 is performed for each characteristic wavelength, and the change rate combined spectral image for each characteristic wavelength. Create Then, the change rate synthesized spectral image for each characteristic wavelength is synthesized to create one change rate synthesized spectral image. Further, when two types of observation parameters are set as described above as a modification, the same processing is performed for each of the first stained sample image and the second stained sample image. The created image data of one or more change rate combined spectral images is stored in the storage unit 55.

  Subsequently, the attention target extraction unit 545 extracts a region of the target tissue from the created change rate combined spectroscopic image, and generates the attention target image (step c5). For example, the region of the target tissue is extracted by appropriately combining known image processing such as smoothing, binarization, edge extraction, and morphology (expansion / contraction) on the change rate combined spectral image. At this time, when the target tissue is a tissue having a characteristic shape such as a nucleus or red blood cell, particle analysis is performed on the change rate composite spectral image to obtain parameters such as area and circularity. It is good as well. According to this, the region of the target tissue can be extracted with higher accuracy. The generated data of the target image is stored in the storage unit 55.

  More specifically, when a plurality of wavelengths are determined as feature wavelengths, the rate-of-change synthesized spectroscopic image for each feature wavelength and the rate-of-change synthesized spectroscopic image for each feature wavelength are synthesized into one sheet as described above. A change rate composite spectral image is created. In addition, when two types of observation parameters are set as described above as a modification, two types of change rate combined spectral images are created. Therefore, the target object extraction unit 545 displays the plurality of change rate combined spectral images on the display unit 52, and selects one change rate combined spectral image manually or automatically according to a user operation. Then, the target object extraction unit 545 performs the process of step c5 on the selected change rate combined spectral image to create a target object image.

  FIG. 13 is a diagram illustrating an example of a change rate combined spectral image selection screen. This change rate combined spectral image selection screen is displayed on the display unit 52 when a plurality of change rate combined spectral images are created. As shown in FIG. 13, a plurality of change rate composite spectral images I201 are displayed side by side in, for example, a thumbnail format on the change rate composite spectral image selection screen. This rate-of-change synthesized spectral image selection screen includes an image display unit W201 that displays one of a plurality of rate-of-change synthesized spectral images as a selected image, and one change selected manually or automatically according to a user operation. The rate-combination spectral image is enlarged and displayed.

  In addition, the change rate combined spectral image selection screen includes radio buttons B201 and B202 that can alternatively select whether to manually select or automatically select one change rate spectral combined image from a plurality of change rate combined spectral images. In addition, an OK button BTN201 for confirming the operation, a cancel button BTN202 for canceling the operation, and the like are arranged.

  For example, when the radio button B201 is selected in FIG. 13, by moving the cursor CS201 via the operation unit 51, one of a plurality of change rate composite spectral images can be manually selected. The change rate combined spectral image selected by the cursor CS201 is enlarged and displayed on the image display unit W201 as a selected image. On the other hand, when the radio button B202 is selected, one of the plurality of change rate combined spectral images is automatically selected, and the selected change rate combined spectral image is enlarged and displayed as a selected image on the image display unit W201. Is done. As an internal process in this case, the target object extraction unit 545 calculates a pixel average value of all the pixels for each change rate combined spectral image. Then, the target object extraction unit 545 selects the change rate combined spectral image that maximizes the calculated pixel average value, and performs display processing on the image display unit W201. The user presses the OK button 201 in a state where a desired change rate combined spectral image is displayed as a selected image on the image display unit W201.

  Note that a spectral image, a change rate spectral image, or a change rate combined spectral image may be displayed on the display unit 52 to perform extraction processing of the target tissue. Then, the threshold value used in the binarization processing applied to the rate-of-change synthesized spectral image, the smoothing, binarization, edge extraction, and morphology (expansion / shrinkage) images used to extract the region of the target tissue It may be configured to accept processing designation.

  FIG. 14 is a diagram illustrating an example of an observation target tissue extraction screen. As shown in FIG. 14, the observation target tissue extraction screen includes a spectroscopic image, a change rate spectroscopic image, or a change rate composite spectroscopic image, and a target image obtained by performing image processing on the change rate composite spectroscopic image. An image display unit W21 that displays side by side is provided, and an arrangement spectral image, a change rate spectral image, or a change rate combined spectral image to be displayed on the left side toward the image display unit W21 can be selected in the list box B21. In FIG. 14, the change rate combined spectral image is selected by the list box B21, the change rate combined spectral image I21 created in step c1 is displayed on the left side of the image display unit W21, and after image processing on the right side. The target image I22 is displayed.

  In addition, on the observation target tissue extraction screen, a slider bar S21 for adjusting contrast, a slider bar S22 for specifying a threshold value used in binarization processing, and image processing to be applied to the change rate composite spectral image are selected. Check box C21, an OK button BTN21 for confirming the operation, a cancel button BTN22 for canceling the operation, and the like. In FIG. 14, as the check box C21, five check boxes for individually selecting seed setting, dilation, contraction, edge extraction, and smoothing are arranged. Here, when the seed setting check box is selected, the pointer P21 is displayed on the target image I22 of the image display unit W21. For example, it is assumed that the user operates the operation unit 51 to move the pointer P21 to a position on the change rate combined spectral image where the target tissue is reflected, and presses the OK button BTN21. In this case, for example, the position of the pointer P21 is set as the start point, and a process of searching for a pixel value similar to the pixel value of the start point in the spectral image is performed, and the region of the target tissue is extracted.

  In this way, the user can set threshold values and specify image processing to be performed on the change rate combined spectral image while viewing the spectral image, change rate spectral image, and change rate combined spectral image.

Subsequently, as illustrated in FIG. 11, the target object extraction unit 545 converts the spectral transmittance value obtained for each pixel of the stained specimen image into an RGB value, and displays a display RGB image (stained specimen RGB image). Create (step c7). Here, if the spectral transmittance at a point (pixel) x on the stained specimen image is T (x), the RGB value G _RGB (x) is expressed by the following equation (6).
G _RGB (x) = HT (x) (6)

Here, H in Expression (6) is a system matrix and is expressed by the following Expression (7).
H = FSE (7)

  F is the spectral transmittance of the tunable filter. S is a spectral sensitivity characteristic of the camera, and a camera spectral characteristic data set (here, data set C-21) corresponding to the characteristic data of the facility selected based on the attribute value of the stained specimen attribute of the observation stained specimen. ) Is used. E is a spectral emission characteristic of lighting per unit time, and a data set of lighting spectral characteristics (here, data set C-01) corresponding to the characteristic data of the selected facility is used. Since the spectral transmittance values are calculated for all the pixel positions x of the stained specimen image, if the process of converting T (x) into RGB values for the image position x is repeated over the entire image, the captured multi An RGB image having the same width and height as the band image is obtained. Data of the created stained specimen RGB image is stored in the storage unit 55.

  Then, the attention object extraction unit 545 creates a virtual special stained image by superimposing the attention object image on the stained specimen RGB image (step c9). The data of the created virtual special stained image is stored in the storage unit 55. FIG. 15 is a diagram illustrating an example of a virtual special stained image. This virtual specially stained image is obtained as an image obtained by applying special staining for staining the target tissue of interest to the specimen, and the region of the target tissue of interest in the stained specimen image can be distinguished with high visibility.

  Note that the region extraction method described here is an example, and the present invention is not limited to this. For example, using a discriminator such as a support vector machine (SVM), the pixel of the target tissue may be extracted by learning discrimination processing using the observation spectral characteristic as a feature amount. For example, the rate of change between wavelengths ω and ω−1 and the rate of change between wavelengths ω and ω + 1 are calculated based on the observed spectral characteristics of the target tissue (in this case, data set B-01, etc.). These are combined to obtain change rate composite data. Then, the pixel of the target tissue may be extracted by performing learning determination processing on the change rate composite spectral image created in step c3 using the obtained change rate composite data as teacher data. At this time, the learning discrimination process may be performed using only the characteristic wavelength as the effective wavelength. According to this, the number of dimensions can be reduced, and the discrimination accuracy can be improved.

  As described above, according to the first embodiment, the characteristic data including spectral characteristics measured for each attribute value of the stained specimen attribute can be stored in the characteristic data storage unit 7 in advance. Then, characteristic data corresponding to the attribute value of the designated observation stained specimen is selected, the characteristic wavelength of the target tissue is determined by analyzing the selected characteristic data, and the observation unit 3 when observing the observation stained specimen System parameters for setting the operating environment can be set. At this time, based on the determined characteristic wavelength of the target tissue of interest, it is possible to set a selection wavelength band (selection wavelength width) of the tunable filter when capturing a stained specimen image. Specifically, the selection wavelength width can be narrowed in the vicinity of the characteristic wavelength, and the system parameters can be set according to the actual environment at the time of observation (when the stained specimen image is captured).

  As a result, since the spectral feature of the target tissue can be acquired with high accuracy, it is possible to appropriately set the system parameters for acquiring the stained specimen image that can extract the region of the target tissue with high accuracy. Therefore, it is possible to automatically set an optimal system environment for acquiring the characteristics of the specimen to be observed, and to obtain a stained observation image that allows easy observation and easy diagnosis of the region of the target tissue. .

  In addition, the stained specimen image acquired according to the set system parameters can be image-processed to extract a region where the target tissue is reflected. Then, it is possible to create a virtual special staining image in which the region of the target tissue and the other region are identified and displayed. For example, when the staining applied to the specimen is insufficient or when there is uneven staining In addition, the region of the target tissue can be distinguished from other tissues with high visibility.

  Conventionally, when it is difficult to visually recognize the region of the target tissue of interest, the stained sample image is repeatedly acquired while directly operating the microscope or the multiband camera until a stained sample image that is easy to observe is obtained. Alternatively, if the acquired stained specimen image is not sufficiently stained and has poor visibility, the laboratory technician is requested to re-stain the specimen. Here, the task of searching for a characteristic wavelength for the target tissue of interest requires skill and a high burden on the user.

  On the other hand, in the first embodiment, the user (pathologist) does not need to operate the microscope or the multiband camera, and is displayed on the display unit 52 at a place different from the place where the observation unit 3 is installed. Observation and diagnosis can be performed while looking at virtual special stained images. In addition, there is no need for a process of requesting a laboratory technician to stain the specimen again and re-staining. Therefore, it is possible to reduce the work burden such as operation of a microscope and a multiband camera when acquiring a stained specimen image, saving a user's trouble. As a result, it is possible to suppress the influence on the diagnosis accuracy when the staining is insufficient, reduce the number of persons involved in diagnosis, shorten the diagnosis time, and realize cost reduction.

  In the above-described embodiment, the characteristic wavelength is automatically determined, the system parameters are set, and the stained specimen image is acquired. On the other hand, the determined characteristic wavelength and the coefficient k in the above formula (5) used when determining the characteristic wavelength may be configured to be changeable according to a user operation.

  FIG. 16 is a diagram illustrating an example of a characteristic wavelength change screen. The characteristic wavelength change screen shown in FIG. 16 includes a slider bar S31 for changing the characteristic wavelength, a slider bar S32 for changing the coefficient k, an OK button BTN31 for confirming the operation on the slider bar S31 and the slider bar S32, A cancel button BTN32 or the like for canceling the operation is arranged. Further, on the characteristic wavelength change screen, a graph G31 similar to the graph of FIG. 16 representing the observation spectral characteristics of the characteristic data selected according to the stained specimen attribute of the observed stained specimen is displayed. Along with the graph G31, the current characteristic wavelength is indicated by a broken line. Here, in the case where a wavelength that is not appropriate as a selection wavelength band (selection wavelength width) of a tunable filter set as one of imaging parameters for a staining dye is determined in advance, this is indicated by a one-dot chain line in FIG. Thus, the wavelength that cannot be changed or the wavelength that cannot be selected may be shown together with the graph G31.

  At this time, for example, the color of the broken line is changed according to the observation spectral characteristic (data set) used when acquiring the determined characteristic wavelength (the wavelength of Kλ (i)), and the characteristic is determined from a different observation spectral characteristic. It is good also as displaying a wavelength so that identification is possible. Alternatively, the line type may be changed for identification display.

  In this characteristic wavelength change screen, the user changes the characteristic wavelength by operating, for example, the slider bar S31. Alternatively, the value of the coefficient k is changed by operating the slider bar S32. Thereafter, when the OK button BTN31 is pressed, if the slider bar S31 is operated, the value is changed as the characteristic wavelength, and the broken line display indicating the characteristic wavelength in the graph G31 is updated according to the changed characteristic wavelength. The On the other hand, if the slider bar S32 is operated, the threshold value Th is changed with the value as the value of the coefficient k. Then, the above-described processing is performed using the threshold Th, the feature candidate wavelengths are acquired again, and the feature wavelengths are determined again. Also in this case, the broken line display indicating the characteristic wavelength in the graph G31 is updated according to the characteristic wavelength after the change.

  According to this modification, the user can directly adjust the characteristic wavelength while checking the observation spectral characteristic graph, or adjust the characteristic wavelength by correcting the value of the coefficient k. Settings can be made more appropriately.

(Embodiment 2)
FIG. 17 is a block diagram illustrating a functional configuration of the microscope observation system 1b according to the second embodiment. In FIG. 17, the same components as those in the microscope observation system 1b described in the first embodiment are denoted by the same reference numerals.

  As shown in FIG. 17, the observation system control unit 5b according to the second embodiment includes an operation unit 51, a display unit 52, a processing unit 54b, and a storage unit 55b.

  The processing unit 54b includes a stained sample attribute specifying unit 541b, a characteristic data labeling unit 546b, a characteristic data analyzing unit 543b, a system environment setting unit 544b, and a stained sample image analyzing unit 547b.

  The stained specimen attribute designating unit 541b designates the attribute value of the stained specimen attribute and the observation magnification for the observed stained specimen according to the user operation. In the second embodiment, a plurality of target organizations can be designated as target organizations that are one of the attribute items. The case where one organization is selected as the target organization has been described in the first embodiment. Hereinafter, a description will be given focusing on the case where two or more organizations are designated as the target organization.

  The characteristic data labeling unit 546b selects one or more characteristic data from the characteristic data stored in the characteristic data storage unit 7 on the basis of the specified stained specimen attribute, and two or more specified Label the characteristic data selected according to the target tissue.

  The characteristic data analysis unit 543b determines the characteristic wavelength of each target tissue based on one or more characteristic data selected by the characteristic data labeling unit 546b.

  The system environment setting unit 544b compares the characteristic wavelengths of the target tissues determined by the characteristic data analysis unit 543b, and sets system parameters so that the sensitivity is improved for a predetermined wavelength band including the characteristic wavelengths. .

  The stained specimen image analysis unit 547b performs image processing on the stained specimen image captured by the stained specimen image capturing unit 33, and individually extracts a region in which each designated target tissue appears from the stained specimen image.

  Further, in the storage unit 55b, an observation system control program for realizing a process of setting a system parameter based on the stained specimen attribute of the observation stained specimen, controlling the operation of the observation section 3, and acquiring a stained specimen image 551b is stored.

  FIG. 18 is a flowchart illustrating a processing procedure performed by the observation system control unit 5b according to the second embodiment. Note that the processing described here is realized by the operation of each unit of the observation system control unit 5b according to the observation system control program 551b stored in the storage unit 55b.

  First, the stained specimen attribute designating unit 541b performs a process of displaying a stained specimen attribute designation screen on the display unit 52 and notifying a designation of designation of a stained specimen attribute, and the stained specimen attribute and observation magnification by the user via the operation unit 51. Is received (step d1).

  FIG. 19 is a diagram illustrating an example of a stained specimen attribute designation screen according to the second embodiment. On the stained specimen attribute designation screen shown in FIG. 19, spin boxes B41, B42, and B44 for designating attribute values of attribute items of staining type, organ, and facility, and the number of target tissues (number of target tissues) Spin box B43 for designating observation, spin boxes B431 and B432 for individually designating the number of target tissues designated by spin box B43, spin box B45 for designating observation magnification, and each of these spins An OK button BTN41 for confirming the operation on the box, a cancel button BTN42 for canceling the operation, a memo column M41, and the like are arranged. In the second embodiment, the user designates the number of tissues to be designated as the target tissue of interest on this stained specimen attribute designation screen and designates the designated number of tissues of interest. In FIG. 19, a maximum of four attention target tissues can be designated, but the number of attention target tissues that can be designated is not particularly limited.

  If the attribute value of the stained specimen attribute is designated, then, as shown in FIG. 18, the characteristic data labeling unit 546b refers to the characteristic data storage unit 7 and notifies the designation request by the stained specimen attribute designation unit 541b. One or more characteristic data corresponding to the stained specimen attribute designated in response is selected (step d3). The selection of characteristic data can be performed in the same manner as in the first embodiment.

Then, the characteristic data labeling unit 546b assigns a label, for example, by assigning a serial number to two or more designated attention target tissues, and labels the selected characteristic data for each of the attention target tissues (step d5). Specifically, the characteristic data labeling unit 546b applies the label L _n (n = 1, 2, 3,..., Attention target tissue) assigned to the attention target tissue for the characteristic data selected according to the attention target tissue. Number). For example, as illustrated in FIG. 19, “elastic fiber” is selected as the first target tissue 1 in the spin box B431, and “cytoplasm” is specified as the second target tissue 2 in the spin box B432. In this case, the label L _{1 is} assigned to the characteristic data selected based on “elastic fiber”, and the label L ₂ is assigned to the characteristic data selected based on “cytoplasm”.

Subsequently, the characteristic data analysis unit 543b determines the characteristic wavelength of the target tissue based on the characteristic data selected in step d3 (step d7). The method for determining the characteristic wavelength can be performed in the same manner as in the first embodiment. However, at this time, the characteristic wavelength is determined for each target tissue. The determined characteristic wavelength is stored in the storage unit 55b in association with the label L _n corresponding to the target tissue of interest.

Subsequently, the system environment setting unit 544b sets system parameters (observation parameters and imaging parameters) based on the characteristic wavelength determined in step d7 (step d9). The system parameter setting method can be performed in the same manner as in the first embodiment. However, at this time, system parameters are set for each target organization. As a result, the selected wavelength band (selected wavelength width) of the tunable filter is set according to the characteristic wavelength determined for each target tissue. The set system parameter is stored in the storage unit 55b in association with the label L _n representing the corresponding target tissue of interest.

  Then, the system environment setting unit 544b outputs the set system parameters to the stained specimen observation unit 31 and the stained specimen image capturing unit 33 of the observation unit 3 to instruct operation of each unit. As a result, the observation unit 3 operates according to the system parameters set by the system environment setting unit 544b, and obtains a stained sample image by performing multiband imaging of the observed image of the stained sample (step d11).

  Subsequently, the stained specimen image analysis unit 547b performs a stained specimen image analysis process, and performs image processing on the stained specimen image to extract a region where the target tissue in the stained specimen image is reflected (step d13). Specifically, the stained specimen image analysis unit 547b extracts a region of the target tissue based on a stained specimen image having a characteristic wavelength common to each target tissue and a stained specimen image having a different characteristic wavelength.

  FIG. 20 is a flowchart showing a detailed processing procedure of the stained specimen image analysis processing. As shown in FIG. 20, in the stained sample image analysis process, the stained sample image analysis unit 547b sequentially processes the target tissue as the processing target, and performs the processing of loop A for each target tissue (step e1 to step e9). .

  In the loop A, the stained specimen image analysis unit 547b firstly uses the characteristic wavelength of the target tissue to be processed and the characteristic wavelength based on the stained specimen image (spectral image) at a wavelength of 1 [nm] before and after the characteristic wavelength. A rate-of-change spectral image for each is created (step e3). The change rate spectral image can be created in the same manner as in the first embodiment.

  Subsequently, the stained specimen image analysis unit 547b synthesizes the created change rate spectral image for each characteristic wavelength to create a change rate synthesized spectral image (step e5). The change rate composite spectral image can be created in the same manner as in the first embodiment. Then, the stained specimen image analysis unit 547b synthesizes the change rate combined spectral image obtained for each characteristic wavelength and creates a total wavelength change rate combined spectral image (step e7).

For example, a case will be described as an example in which _two target tissues of “elastic fiber” and “cytoplasm” are designated as described above, and labels L ₁ and L ₂ are assigned to the target tissues of interest. Here, the characteristic wavelength determined for the target tissue “elastic fiber” to which the label L ₁ is assigned is Λ1 _n (n = 1, 2, 3,...), And the target tissue to which the label L ₂ is assigned. _Let the characteristic wavelength determined for “cytoplasm” be Λ2 _n (n = 1, 2, 3,...). In this case, the characteristic wavelength Λ1 _n (n = 1, 2, 3,...) Of the target tissue “elastic fiber” of the label L ₁ and the wavelength Λ1 _n ± 1 of 1 [nm] before and after this characteristic wavelength. Based on the stained specimen image (spectral image) at (n = 1, 2, 3,...), A change rate spectroscopic image is created, and based on this change rate spectroscopic image, a change rate composite spectroscopic image is generated. Create and obtain a total spectral change rate spectral image. Similarly, the characteristic wavelength Λ2 _n (n = 1, 2, 3,...) Of the target tissue “cytoplasm” of the label L ₂ and the wavelength Λ2 _n ± 1 (n = 1 nm before and after this characteristic wavelength) 1, 2, 3, ...) based on the stained specimen image (spectral image), create a change rate spectral image, create a change rate composite spectral image based on this change rate spectral image, A total spectral change rate spectral image is obtained.

Subsequently, as illustrated in FIG. 20, the stained specimen image analysis unit 547b creates a logical difference light image based on the total wavelength change rate combined spectral image obtained for each target tissue (step e11). FIG. 21 is a diagram illustrating a process of creating a logical difference optical image. FIG. 21 (a) shows an example of a full wavelength change rate composite spectral image obtained for the label L ₁ of the target tissue of interest "elastic fibers". Further, FIG. 21 (b) shows an example of a full wavelength change rate composite spectral image obtained for the target tissue of interest "cytoplasmic" label L _2. FIG. 21C is a diagram illustrating an example of a logical difference light image obtained by combining the total wavelength change rate combined spectroscopic image of FIG. 21A and the total wavelength change rate composite spectroscopic image of FIG.

Here, the stained specimen image analysis unit 547b obtains the target tissue “cytoplasm” of the label L ₂ from each pixel of the total wavelength change rate composite spectral image obtained for the target tissue “elastic fiber” of the label L ₁ . For example, the pixel value of a pixel whose pixel value is equal to or greater than a preset threshold value T in the all-wavelength change rate combined spectral image is set to “0 (zero)” to obtain a logical difference light image. Similarly, a logical difference light image is created for the total wavelength change rate combined spectroscopic image obtained for the target tissue “cytoplasm” of the label L ₂ . By creating this logical difference light image, the features common to the all-wavelength change rate combined spectral images of the other target tissue can be removed, so that the features of each target tissue can be reproduced more accurately. This logical difference light image is obtained as an image in which pixels having a large rate of change between wavelengths that do not overlap with other target tissue are emphasized.

  Subsequently, the stained specimen image analysis unit 547b extracts a corresponding target tissue region from the created logical difference light image for each target tissue, and generates a target image (step e13). For example, a known image processing such as smoothing, binarization, edge extraction, and morphology (expansion / contraction) is appropriately and appropriately combined with the logical difference light image in the same manner as in the first embodiment, and the target tissue of interest Extract the region. Thereafter, the stained specimen image analysis unit 547b creates a stained specimen RGB image in the same manner as in the first embodiment (step e15), and creates a virtual special stained image by superimposing the stained specimen RGB image and the target image. (Step e17). For example, a virtual special stained image in which a stained specimen RGB image and an image of interest about “elastic fiber” are superimposed is obtained as an image obtained by applying special staining for staining “elastic fiber” to the specimen. The region of “elastic fiber” inside can be distinguished with good visibility. For the “cytoplasm”, a similar virtual special stained image is obtained, and the “cytoplasm” region in the stained specimen image can be distinguished with high visibility.

  As described above, according to the second embodiment, the same effects as those of the first embodiment can be obtained, and each target target tissue can be identified from the stained specimen image even when a plurality of target target tissues are designated. It is possible to create a logical difference light image in which the regions to be reflected are individually extracted and the regions of each target tissue and the other regions are identified and displayed. Then, with this logical difference light image, the region of each target tissue can be distinguished from other target tissue and other tissues with high visibility.

1 is a schematic diagram illustrating an overall configuration of a microscope observation system according to a first embodiment. 2 is a block diagram illustrating a functional configuration of the microscope observation system according to Embodiment 1. FIG. It is a figure explaining the data structural example of characteristic data. It is another figure explaining the example of a data structure of characteristic data. It is another figure explaining the example of a data structure of characteristic data. 4 is a flowchart illustrating a processing procedure performed by an observation system control unit according to the first embodiment. 6 is a diagram illustrating an example of a stained specimen attribute designation screen according to Embodiment 1. FIG. It is a flowchart which shows the detailed process sequence of a characteristic wavelength candidate determination process. It is a flowchart which shows the detailed process sequence of a characteristic wavelength determination process. It is a figure which shows an example of the characteristic wavelength confirmation screen. It is a flowchart which shows the detailed process sequence of an attention object extraction process. It is a figure which shows an example of a change rate synthetic | combination spectral image. It is a figure which shows an example of a change rate synthetic | combination spectral image selection screen. It is a figure which shows an example of an observation object organization extraction screen. It is a figure which shows an example of a virtual special dyeing | staining image. It is a figure which shows an example of the characteristic wavelength change screen. 6 is a block diagram illustrating a functional configuration of a microscope observation system according to a second embodiment. FIG. 10 is a flowchart illustrating a processing procedure performed by the observation system control unit according to the second embodiment. FIG. 10 is a diagram showing an example of a stained specimen attribute designation screen in the second embodiment. It is a flowchart which shows the detailed process sequence of a stained specimen image analysis process. It is a figure explaining the creation process of a logical difference optical image.

DESCRIPTION OF SYMBOLS 1,1b Microscope observation system 3 Observation part 31 Stained specimen observation part 33 Stained specimen image imaging part 5,5b Observation system control part 51 Operation part 52 Display part 54,54b Processing part 541,541b Stained specimen attribute designation part 542 Characteristic data selection Unit 543, 543b characteristic data analysis unit 544, 544b system environment setting unit 545 attention object extraction unit 546b characteristic data labeling unit 547b stained specimen image analysis unit 55, 55b storage unit 551, 551b observation system control program 7 characteristic data storage unit

Claims (7)

A sample observation unit configured by a microscope, a sample image imaging unit configured by a multiband camera capable of selecting a wavelength or wavelength band to be imaged and imaging a sample, and an observation unit for observing the sample;
An observation system control unit for controlling the operation of the observation unit;
A characteristic data storage unit which is determined according to an attribute value indicating the attribute of the sample and stores characteristic data including spectral characteristic information measured in advance for each attribute value;
With
The observation system controller is
A sample attribute specifying unit for specifying an attribute value of a sample to be observed;
A characteristic data selection unit that selects at least one characteristic data corresponding to the attribute value designated by the sample attribute designation unit from the characteristic data stored in the characteristic data storage unit;
Analyzing the characteristic data selected by the characteristic data selection unit, and based on the spectral characteristic information of the characteristic data, a characteristic data analysis unit for determining a characteristic wavelength for the specimen to be observed ;
Set based on the analysis result of the characteristic data analyzing unit, the imaging parameters for setting the observation parameters and / or operating environment of the sample image capturing unit for setting an operation environment of the specimen observation section as a system parameter A system environment setting unit configured to set, as the imaging parameter, a wavelength or wavelength band captured by the sample image capturing unit based on the characteristic wavelength ;
A microscope observation system comprising: The microscope observation system according to claim 1 , wherein the system environment setting unit sets an exposure time of the multiband camera as the imaging parameter based on the wavelength or wavelength band to be imaged. The observation system control unit includes an attention target extraction unit that extracts a region of interest from a sample image captured by the sample image capturing unit in accordance with a system parameter set by the system environment setting unit. Item 3. The microscope observation system according to Item 1 or 2. 4. The microscope observation system according to claim 3 , wherein the target object extraction unit displays the region of the target object in the sample image so as to be identifiable, and performs display processing on the display unit. The specimen is a biological tissue specimen stained with a predetermined pigment,
Attributes related to the specimen include the type of staining applied to the biological tissue specimen, the organ from which the biological tissue specimen is collected, the tissue of interest in the biological tissue specimen, and the facility where the biological tissue specimen is stained The microscope observation system according to claim 3 , further comprising an attribute item. The attention object extraction unit extracts, as the attention object region, an area in which the tissue of the attention object specified by the sample attribute specification unit as the attribute value of the observation target sample is reflected, and the attention image is extracted in the sample image. 6. The microscope observation system according to claim 5 , wherein a virtual specially stained image in which an area in which a target tissue is shown is identified and displayed is created. The specimen attribute designating unit designates two or more tissues as the target tissue of interest,
The target object extracting unit extracts, as the target target areas, regions where two or more target target tissues specified by the sample attribute specifying unit are shown, and the two or more target target tissues in the sample image The microscope observation system according to claim 5 , wherein the virtual special stained image is created by selectively identifying and displaying one of the regions.