WO2018143406A1 - Dispositif et programme de traitement d'images - Google Patents

Dispositif et programme de traitement d'images Download PDF

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Publication number
WO2018143406A1
WO2018143406A1 PCT/JP2018/003587 JP2018003587W WO2018143406A1 WO 2018143406 A1 WO2018143406 A1 WO 2018143406A1 JP 2018003587 W JP2018003587 W JP 2018003587W WO 2018143406 A1 WO2018143406 A1 WO 2018143406A1
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image
fluorescent
cell
expression
expression pattern
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PCT/JP2018/003587
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English (en)
Japanese (ja)
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由佳 吉原
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コニカミノルタ株式会社
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Publication of WO2018143406A1 publication Critical patent/WO2018143406A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material

Definitions

  • the present invention relates to an image processing apparatus and a program.
  • the degree of malignancy of a lesion is determined on the basis of an expression pattern such as an expression level of a specific biological substance in a tissue specimen and a distribution in a cell.
  • an expression pattern such as an expression level of a specific biological substance in a tissue specimen and a distribution in a cell.
  • the presence or absence of expression of a specific biological material for each cell is determined from a microscopic image obtained by photographing a tissue specimen stained by an immunohistochemical method using an enzyme (DAB) or a fluorescent substance, Used for diagnosis.
  • DAB enzyme
  • HER2 protein is a glycoprotein present on the cell surface and is involved in cell proliferation and differentiation in normal cells, but when overexpressed, it is involved in malignant cell formation and acts as an oncogene.
  • anti-HER2 therapy a technique capable of accurately detecting overexpression of HER2 protein in a patient tissue sample is indispensable.
  • breast cancer is classified into five subtypes based on the combination of the presence or absence of expression of hormone receptors (estrogen receptor (ER) and progesterone receptor (PgR)), HER2, and Ki67. Since the properties of cancer cells differ depending on the subtype, it is necessary to select appropriate drug therapies (for example, chemotherapy, hormone therapy, anti-HER2 therapy), so it is necessary to confirm the expression patterns of these biological substances, respectively. There is.
  • Macrophages are important cells that form a microenvironment of cancer together with fibroblasts and vascular endothelial cells, and it is known that many macrophages exist around cancer cells. Macrophages are divided into two phenotypes, which are pro-inflammatory M1 type and anti-inflammatory M2 type, which have completely different physiological roles. Macrophages infiltrating tumor tissue are tumor-associated macrophages (Tumor-associated macrophages). , TAM).
  • TAM is known to consist mainly of M2 macrophage populations, and TAM is known to promote cell proliferation and cancer metastasis by effectively suppressing T cell activity and regulating signal transduction. It has been. Clinical studies have also revealed an association between TAM status and poor prognosis in human tumors, and TAM is now considered a promising target for tumor therapy. Therefore, there is a demand for a technique for detecting a target protein in macrophages with high accuracy.
  • Patent Document 1 discloses a technique in which a plurality of biological materials are dyed with pigments of different colors and the expression pattern of each biological material is observed. That is, a microscopic image of a tissue specimen stained with cells and biological materials is acquired, and an image stained with cells and an image stained with biological materials are superimposed using an image processing apparatus, and the expression sites of the cells and biological materials are respectively determined. Extract from image. By comparing the dye amount of the stained biological material with a predetermined threshold, the expression pattern is classified for each cell, and the classification result is displayed on the display for observation. According to this method, the expression patterns of a plurality of biological substances can be observed on the same screen, and the diagnostic accuracy can be improved.
  • the present invention has been made in view of the above problems, and an object thereof is to provide an image processing apparatus and program capable of quantitatively evaluating an expression pattern of a biological substance in a tissue specimen.
  • An input means for inputting a morphological image representing the morphology of a cell and a fluorescent image representing the expression of the biological material in the same range as the morphological image with a fluorescent luminescent spot in a tissue specimen stained with a single or plural types of biological materials; , First extraction means for extracting a cell region from the morphological image; Second extraction means for extracting a fluorescent bright spot region from the fluorescent image; Generating means for calculating an expression level of the biological material extracted from the number of the fluorescent bright spot regions extracted by the second extraction means, and generating expression pattern information including the expression level; Classification means for classifying cells according to the expression pattern information generated by the generation means.
  • the invention according to claim 2 is the image processing apparatus according to claim 1, Display control means for displaying the expression pattern information generated by the generation means on a display means; The display control means displays the expression pattern information and the morphological image in a superimposed manner.
  • the invention according to claim 3 is the image processing apparatus according to claim 2,
  • the display control means changes and displays the expression pattern information display method for each cell class classified by the classification means.
  • the invention according to claim 4 is the image processing apparatus according to claim 2 or 3,
  • the display control means displays the expression pattern information so as not to overlap each other.
  • the invention according to claim 5 is the image processing apparatus according to any one of claims 2 to 4,
  • the display control means displays the expression pattern information in a color different from the color of the morphological image.
  • the invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5, A specifying means for specifying a cell type based on the feature amount of the cell region extracted by the first extracting means is provided.
  • the program according to claim 7 is: The computer of the image processing device Input means for inputting a morphological image representing cell morphology and a fluorescent image representing the expression of the biological material in the same range as the morphological image with fluorescent luminescent spots in a tissue specimen stained with a single or plural types of biological materials, First extraction means for extracting a cell region from the morphological image; Second extraction means for extracting a fluorescent bright spot region from the fluorescent image; Generating means for calculating an expression level of the biological material extracted from the number of the fluorescent bright spot regions extracted by the second extraction means, and generating expression pattern information including the expression level; Classification means for classifying cells according to the expression pattern information generated by the generation means, To function as.
  • an image processing apparatus and program capable of quantitatively evaluating the expression pattern of a biological substance in a tissue specimen.
  • FIG. 1 It is a figure which shows the system configuration
  • FIG.5 S5 It is a figure which shows the image from which the fluorescence image was extracted. It is a figure which shows the image from which the luminescent spot area
  • FIG. 1 shows an example of the overall configuration of the pathological diagnosis support system 100.
  • the pathological diagnosis support system 100 acquires a microscopic image of a tissue section of a human body stained with a predetermined staining reagent, and analyzes the acquired microscopic image, thereby expressing the expression of a specific biological material in the tissue section to be observed. This is a system that outputs feature quantities quantitatively.
  • the pathological diagnosis support system 100 is configured by connecting a microscope image acquisition apparatus 1A and an image processing apparatus 2A so that data can be transmitted and received via an interface such as a cable 3A.
  • the connection method between the microscope image acquisition apparatus 1A and the image processing apparatus 2A is not particularly limited.
  • the microscope image acquisition device 1A and the image processing device 2A may be connected via a LAN (Local Area Network) or may be connected wirelessly.
  • the microscope image acquisition apparatus 1A is a known camera-equipped microscope, which acquires a microscope image of a tissue section on a slide placed on a slide fixing stage and transmits it to the image processing apparatus 2A.
  • the microscope image acquisition apparatus 1A includes an irradiation unit, an imaging unit, an imaging unit, a communication I / F, and the like.
  • the irradiating means includes a light source, a filter, and the like, and irradiates the tissue section on the slide placed on the slide fixing stage with light.
  • the imaging means is composed of an eyepiece lens, an objective lens, and the like, and forms an image of transmitted light, reflected light, or fluorescence emitted from the tissue section on the slide by the irradiated light.
  • the image pickup means is a microscope-installed camera that includes a CCD (Charge Coupled Device) sensor and the like, picks up an image formed on the image forming surface by the image forming means, and generates digital image data of the microscope image.
  • the communication I / F transmits image data of the generated microscope image to the image processing apparatus 2A.
  • the microscope image acquisition apparatus 1A includes a bright field unit that combines an irradiation unit and an imaging unit suitable for bright field observation, and a fluorescence unit that combines an irradiation unit and an imaging unit suitable for fluorescence observation. It is possible to switch between bright field / fluorescence by switching.
  • what installed the camera in well-known arbitrary microscopes can be used as 1 A of microscope image acquisition apparatuses.
  • the microscope image acquisition apparatus 1A is not limited to a microscope with a camera.
  • a virtual microscope slide creation apparatus for example, a special microscope scan apparatus that acquires a microscope image of an entire tissue section by scanning a slide on a microscope slide fixing stage). Table 2002-514319
  • the virtual microscope slide creation device it is possible to acquire image data that allows a display unit to view a whole tissue section on a slide at a time.
  • the image processing apparatus 2A calculates the expression distribution of a specific biological material in the tissue section to be observed by analyzing the microscope image transmitted from the microscope image acquisition apparatus 1A.
  • FIG. 2 shows a functional configuration example of the image processing apparatus 2A.
  • the image processing apparatus 2 ⁇ / b> A includes a control unit 21, an operation unit 22, a display unit 23, a communication I / F 24, a storage unit 25, and the like, and each unit is connected via a bus 26. Yes.
  • the control unit 21 includes a CPU (Central Processing Unit) and a RAM (Random Access Memory). ) And the like, and various processes are executed in cooperation with various programs stored in the storage unit 25, and the operation of the image processing apparatus 2A is comprehensively controlled.
  • the control unit 21 executes image analysis processing in cooperation with the image processing program stored in the storage unit 25, and includes a first extraction unit, a second extraction unit, a generation unit, a classification unit, a display control unit, and A function as a specifying means is realized.
  • the operation unit 22 includes a keyboard having character input keys, numeric input keys, various function keys, and the like, and a pointing device such as a mouse, and includes a key press signal pressed by the keyboard and an operation signal by the mouse. Is output to the control unit 21 as an input signal.
  • the display unit 23 includes a monitor such as a CRT (Cathode Ray Tube) or an LCD (Liquid Crystal Display), and displays various screens in accordance with display signal instructions input from the control unit 21. A function as a means is realized.
  • a monitor such as a CRT (Cathode Ray Tube) or an LCD (Liquid Crystal Display)
  • LCD Liquid Crystal Display
  • the communication I / F 24 is an interface for transmitting and receiving data to and from external devices such as the microscope image acquisition device 1A.
  • the communication I / F 24 realizes a function as an input unit for fluorescent images and morphological images.
  • the storage unit 25 is configured by, for example, an HDD (Hard Disk Drive), a semiconductor nonvolatile memory, or the like. As described above, the storage unit 25 stores various programs and various data.
  • the image processing apparatus 2A may include a LAN adapter, a router, and the like, and be connected to an external device via a communication network such as a LAN.
  • the image processing apparatus 2A is, for example, a fluorescence image that is transmitted from the microscope image acquisition apparatus 1A and that expresses the expression of a specific biological material in a cell with a fluorescent bright spot, and the form of the whole cell, cell nucleus, cell membrane It is preferable to perform the analysis using a morphological image (for example, a bright field image) representing the form of a predetermined structure of the cell.
  • a morphological image for example, a bright field image
  • the “bright field image” refers to, for example, a tissue section stained with a hematoxylin staining reagent (H staining reagent) or a hematoxylin-eosin staining reagent (HE staining reagent) and magnified in a bright field in the microscope image acquisition apparatus 1A. It is a microscope image obtained by image and imaging
  • FIG. 3 shows an example of a bright field image.
  • Hematoxylin (H) is a blue-violet pigment that stains cell nuclei, bone tissue, part of cartilage tissue, serous components, etc. (basophilic tissue, etc.).
  • Eodine (E) is a red to pink pigment that stains cytoplasm, connective tissue of soft tissues, erythrocytes, fibrin, endocrine granules (acidophilic tissues, etc.).
  • tissue sections are stained with a fluorescent staining reagent that can specifically stain the structure to be diagnosed of cells, and the fluorescence emitted by the fluorescent staining reagent used is photographed. Fluorescent images may be used.
  • Examples of the fluorescent staining reagent that can be used for obtaining a morphological image include DAPI staining capable of staining cell nuclei, Papalonikolou staining capable of staining cytoplasm, and the like.
  • a “fluorescence image” that expresses the expression of a specific biological substance in a cell as a fluorescent bright spot is a fluorescence obtained by irradiating a tissue section stained with a fluorescent staining reagent with excitation light having a predetermined wavelength in the microscope image acquisition apparatus 1A. It is a microscope image obtained by emitting a substance and enlarging and photographing this fluorescence.
  • FIG. 4 shows an example of the fluorescence image.
  • the fluorescent staining reagent refers to fluorescent nanoparticles that specifically bind to and / or react with a specific biological substance.
  • the “fluorescent nanoparticle” is a nano-sized particle that emits fluorescence when irradiated with excitation light, as will be described in detail later, and is sufficient to express a specific biological substance as a bright spot one molecule at a time. It is a particle capable of emitting intense fluorescence.
  • the fluorescent nanoparticles preferably, quantum dots (semiconductor nanoparticles) or fluorescent substance-containing nanoparticles are used.
  • nanoparticles having an emission wavelength within the sensitivity range of the imaging means of the microscope image acquisition apparatus 1A specifically, nanoparticles having an emission wavelength of 400 to 700 nm are used.
  • a fluorescent staining reagent for obtaining a fluorescent image in which the expression of a specific biological substance specifically expressed in cells is expressed by a fluorescent luminescent spot and a staining method of a tissue section using the fluorescent staining reagent will be described.
  • Fluorescent substance examples include fluorescent organic dyes and quantum dots (semiconductor particles). When excited by ultraviolet to near infrared light having a wavelength in the range of 200 to 700 nm, it preferably emits visible to near infrared light having a wavelength in the range of 400 to 1100 nm.
  • fluorescent organic dyes include fluorescein dye molecules, rhodamine dye molecules, Alexa Fluor (Invitrogen) dye molecules, BODIPY (Invitrogen) dye molecules, cascade dye molecules, coumarin dye molecules, and eosin dyes.
  • fluorescent organic dyes include fluorescein dye molecules, rhodamine dye molecules, Alexa Fluor (Invitrogen) dye molecules, BODIPY (Invitrogen) dye molecules, cascade dye molecules, coumarin dye molecules, and eosin dyes.
  • examples include molecules, NBD dye molecules, pyrene dye molecules, Texas Red dye molecules, and cyanine dye molecules.
  • quantum dots quantum dots containing II-VI group compounds, III-V group compounds, or group IV elements as components ("II-VI group quantum dots”, "III-V group quantum dots”, “ Or “Group IV quantum dots”). These quantum dots may be used alone or in combination of a plurality of types. Specific examples include, but are not limited to, CdSe, CdS, CdTe, ZnSe, ZnS, ZnTe, InP, InN, InAs, InGaP, GaP, GaAs, Si, and Ge.
  • a quantum dot having the above quantum dot as a core and a shell provided thereon.
  • the core is CdSe and the shell is ZnS
  • CdSe / ZnS when the core is CdSe and the shell is ZnS, it is expressed as CdSe / ZnS.
  • CdSe / ZnS, CdS / ZnS , InP / ZnS, InGaP / ZnS, Si / SiO 2, Si / ZnS, Ge / GeO 2, Ge / ZnS and the like may be used, but are not limited to.
  • Quantum dots may be subjected to surface treatment with an organic polymer or the like as necessary.
  • organic polymer or the like as necessary. Examples thereof include CdSe / ZnS having a surface carboxy group (manufactured by Invitrogen), CdSe / ZnS having a surface amino group (manufactured by Invitrogen), and the like.
  • Fluorescent substance-encapsulated nanoparticles are nanoparticles encapsulating the fluorescent substance as described above, and more specifically, those in which the fluorescent substance is dispersed inside the nanoparticles, The fluorescent substance and the nanoparticles themselves may be chemically bonded or may not be bonded.
  • the material constituting the nanoparticles is not particularly limited, and examples thereof include silica, polystyrene, polylactic acid, and melamine.
  • the fluorescent substance-containing nanoparticles can be produced by a known method.
  • silica nanoparticles encapsulating a fluorescent organic dye can be synthesized with reference to the synthesis of FITC-encapsulated silica particles described in Langmuir 8, Vol. 2921 (1992).
  • Various fluorescent organic dye-containing silica nanoparticles can be synthesized by using a desired fluorescent organic dye in place of FITC.
  • Silica nanoparticles encapsulating quantum dots can be synthesized with reference to the synthesis of CdTe-encapsulated silica nanoparticles described in New Journal of Chemistry, Vol. 33, p. 561 (2009).
  • Polystyrene nanoparticles encapsulating a fluorescent organic dye may be copolymerized using an organic dye having a polymerizable functional group described in US Pat. No. 4,326,008 (1982) or polystyrene described in US Pat. No. 5,326,692 (1992). It can be produced using a method of impregnating nanoparticles with a fluorescent organic dye.
  • the polymer nanoparticles encapsulating the quantum dots can be prepared by using the method of impregnating the quantum nanoparticles into polystyrene nanoparticles described in Nature Biotechnology, Vol. 19, page 631 (2001).
  • the average particle diameter is obtained by taking an electron micrograph using a scanning electron microscope (SEM), measuring the cross-sectional area of a sufficient number of particles, and taking each measured value as the area of the circle. It is the value calculated as. In this embodiment, the arithmetic average of the particle diameters of 1000 particles is defined as the average particle diameter.
  • the coefficient of variation is also a value calculated from the particle size distribution of 1000 particles.
  • fluorescent nanoparticle and biological material recognition site are directly bonded in advance as a fluorescent staining reagent that specifically binds and / or reacts with a specific biological material.
  • a “biological substance recognition site” is a site that specifically binds and / or reacts with a specific biological material.
  • the specific biological substance is not particularly limited as long as a substance that specifically binds to the specific biological substance exists, but typically includes proteins (peptides) and nucleic acids (oligonucleotides, polynucleotides). It is done.
  • examples of the biological substance recognition site include an antibody that recognizes the protein as an antigen, another protein that specifically binds to the protein, and a nucleic acid having a base sequence that hybridizes to the nucleic acid.
  • Specific biological substance recognition sites include anti-HER2 antibody that specifically binds to HER2, which is a protein present on the cell surface, anti-ER antibody that specifically binds to estrogen receptor (ER) present in the cell nucleus, cells An anti-actin antibody that specifically binds to actin forming the skeleton is exemplified.
  • anti-HER2 antibody and anti-ER antibody combined with fluorescent nanoparticles are preferable because they can be used for breast cancer medication selection.
  • the mode of binding between the biological substance recognition site and the fluorescent nanoparticle is not particularly limited, and examples thereof include covalent bonding, ionic bonding, hydrogen bonding, coordination bonding, physical adsorption, and chemical adsorption.
  • a bond having a strong bonding force such as a covalent bond is preferable from the viewpoint of bond stability.
  • an organic molecule that connects the biological substance recognition site and the fluorescent nanoparticle.
  • a polyethylene glycol chain can be used, and SM (PEG) 12 manufactured by Thermo Scientific can be used.
  • a silane coupling agent that is a compound widely used for bonding an inorganic substance and an organic substance can be used.
  • This silane coupling agent is a compound having an alkoxysilyl group that gives a silanol group by hydrolysis at one end of the molecule and a functional group such as a carboxyl group, an amino group, an epoxy group, an aldehyde group at the other end, Bonding with an inorganic substance through an oxygen atom of the silanol group.
  • silane coupling agent having a polyethylene glycol chain (for example, PEG-silane no. SIM6492.7 manufactured by Gelest), etc. Is mentioned.
  • silane coupling agent you may use 2 or more types together.
  • a known procedure can be used for the reaction procedure of the fluorescent organic dye-encapsulated silica nanoparticles and the silane coupling agent.
  • the obtained fluorescent organic dye-encapsulated silica nanoparticles are dispersed in pure water, aminopropyltriethoxysilane is added, and the mixture is reacted at room temperature for 12 hours.
  • fluorescent organic dye-encapsulated silica nanoparticles whose surface is modified with an aminopropyl group can be obtained by centrifugation or filtration.
  • the antibody can be bound to the fluorescent organic dye-encapsulated silica nanoparticles via an amide bond.
  • a condensing agent such as EDC (1-Ethyl-3- [3-Dimethylaminopropyl] carbohydrate Hydrochloride: Pierce (registered trademark)
  • EDC 1-Ethyl-3- [3-Dimethylaminopropyl] carbohydrate Hydrochloride: Pierce (registered trademark)
  • a linker compound having a site that can be directly bonded to the fluorescent organic dye-encapsulated silica nanoparticles modified with organic molecules and a site that can be bonded to the molecular target substance can be used.
  • sulfo-SMCC Sulfosuccinimidyl 4 [N-maleimidomethyl] -cyclohexane-1-carboxylate: manufactured by Pierce
  • sulfo-SMCC Sulfosuccinimidyl 4 [N-maleimidomethyl] -cyclohexane-1-carboxylate: manufactured by Pierce
  • fluorescent substance-encapsulated polystyrene nanoparticles When binding a biological material recognition site to fluorescent substance-encapsulated polystyrene nanoparticles, the same procedure can be applied regardless of whether the fluorescent substance is a fluorescent organic dye or a quantum dot. That is, by impregnating a fluorescent nanopigment or quantum dot into polystyrene nanoparticles having a functional group such as an amino group, fluorescent substance-containing polystyrene nanoparticles having a functional group can be obtained, and thereafter using EDC or sulfo-SMCC. Thus, antibody-bound fluorescent substance-encapsulated polystyrene nanoparticles can be produced.
  • biological substance recognition sites include M. actin, MS actin, SM actin, ACTH, Alk-1, ⁇ 1-antichymotrypsin, ⁇ 1-antitrypsin, AFP, bcl-2, bcl-6, ⁇ -catenin, BCA 225 , CA19-9, CA125, calcitonin, calretinin, CD1a, CD3, CD4, CD5, CD8, CD10, CD15, CD20, CD21, CD23, CD30, CD31, CD34, CD43, CD45, CD45R, CD56, CD57, CD61, CD68 , CD79a, "CD99, MIC2", CD138, chromogranin, c-KIT, c-MET, collagen type IV, Cox-2, cyclin D1, keratin, cytokeratin (high molecular weight), pankeratin, pankeratin, cytokeratin 5 / 6, Cytokeratin 7, Cytokeratin 8, Cytokeratin 8
  • the fluorescent nanoparticles may be used by directly binding to the biological material recognition site in advance as described above, or may be indirectly bound to the biological material recognition site in the staining step as in a known indirect method in immunostaining. good. Specifically, for example, a tissue specimen is reacted with a biotinylated primary antibody having a specific biological substance as an antigen, and then further reacted with a staining reagent to which fluorescent nanoparticles modified with streptavidin are bound. Alternatively, staining may be performed using the fact that streptavidin and biotin specifically bind to form a complex.
  • a fluorescent nanoparticle modified with streptavidin after reacting a tissue sample with a primary antibody having a specific protein as an antigen and further reacting with a biotinylated secondary antibody having the primary antibody as an antigen. You may make it react and dye
  • the method for preparing tissue sections is not particularly limited, and those prepared by known methods can be used.
  • the following staining method is not limited to a pathological tissue section, but can also be applied to cultured cells.
  • a tissue section is immersed in a container containing xylene to remove paraffin.
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the immersion time is preferably 3 minutes or longer and 30 minutes or shorter. If necessary, xylene may be exchanged during the immersion.
  • the tissue section is immersed in a container containing ethanol to remove xylene.
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the immersion time is preferably 3 minutes or longer and 30 minutes or shorter. If necessary, ethanol may be exchanged during the immersion.
  • the tissue section is immersed in a container containing water to remove ethanol.
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the immersion time is preferably 3 minutes or longer and 30 minutes or shorter. If necessary, water may be exchanged during the immersion.
  • the activation process of the biological material of a tissue section is performed according to a known method.
  • the activation conditions are not particularly defined, but as the activation liquid, 0.01 M citrate buffer (pH 6.0), 1 mM EDTA solution (pH 8.0), 5% urea, 0.1 M Tris-HCl buffer, etc. are used. be able to.
  • An autoclave, a microwave, a pressure cooker, a water bath, etc. can be used for a heating apparatus.
  • the temperature is not particularly limited, but can be performed at room temperature. The temperature can be 50 to 130 ° C. and the time can be 5 to 30 minutes.
  • the tissue section after the activation treatment is immersed in a container containing PBS (Phosphate Buffered Saline) and washed.
  • PBS Phosphate Buffered Saline
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the immersion time is preferably 3 minutes or longer and 30 minutes or shorter. If necessary, PBS may be exchanged during immersion.
  • each fluorescent nanoparticle PBS dispersion may be mixed in advance or separately placed on the tissue section separately. May be.
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the reaction time is preferably 30 minutes or more and 24 hours or less.
  • a known blocking agent such as BSA-containing PBS
  • a fluorescent staining reagent it is preferable to drop a known blocking agent such as BSA-containing PBS before staining with a fluorescent staining reagent.
  • the stained tissue section is immersed in a container containing PBS to remove unreacted fluorescent nanoparticles.
  • the temperature is not particularly limited, but can be performed at room temperature.
  • the immersion time is preferably 3 minutes or longer and 30 minutes or shorter. If necessary, PBS may be exchanged during immersion.
  • a cover glass is placed on the tissue section and sealed. A commercially available encapsulant may be used as necessary.
  • a microscope image (fluorescence image) is acquired from the stained tissue section using the microscope image acquisition device 1A.
  • the excitation light source and the fluorescence detection optical filter those corresponding to the absorption maximum wavelength and the fluorescence wavelength of the fluorescent material used in the fluorescent staining reagent are appropriately selected.
  • ⁇ Operation of Pathological Diagnosis Support System 100 an operation of acquiring and analyzing the above-described fluorescence image and bright field image in the pathological diagnosis support system 100 will be described.
  • staining was performed using a staining reagent containing fluorescent substance-containing nanoparticles bound to a biological substance recognition site that recognizes a specific protein (for example, Ki67 protein in breast cancer tissue, hereinafter referred to as a specific protein).
  • a tissue specimen is an observation target will be described as an example.
  • the present invention is not limited to this, and in the present invention, a plurality of types of biological substances can be dyed using fluorescent nanoparticles having different light emission characteristics and observed on the same screen.
  • the operator uses two types of staining reagents, a HE staining reagent and a staining reagent using fluorescent substance-encapsulated nanoparticles bound with a biological substance recognition site that recognizes a specific protein as a fluorescent labeling material. Stain. Thereafter, in the microscope image acquisition apparatus 1A, a bright field image and a fluorescence image are acquired by the procedures (a1) to (a5). (A1) An operator places a tissue specimen stained with a hematoxylin staining reagent and a staining reagent containing fluorescent substance-containing nanoparticles on a slide, and places the slide on a slide fixing stage of the microscope image acquisition apparatus 1A.
  • (A2) Set to the bright field unit, adjust the imaging magnification and focus, and place the observation target area on the tissue in the field of view.
  • (A3) Shooting is performed by the imaging unit to generate bright field image data, and the image data is transmitted to the image processing apparatus 2A.
  • (A4) Change the unit to a fluorescent unit.
  • (A5) Shooting is performed by the imaging means without changing the field of view and the shooting magnification to generate image data of a fluorescent image, and the image data is transmitted to the image processing apparatus 2A.
  • step (a5) is repeated when a plurality of types of biological substances are stained.
  • a combination suitable for the emission characteristics is appropriately selected.
  • FIG. 5 shows a flowchart of image analysis processing in the image processing apparatus 2A.
  • the image analysis processing shown in FIG. 5 is executed in cooperation with the control unit 21 and the program stored in the storage unit 25.
  • step S2 when a bright field image is input from the microscope image acquisition device 1A through the communication I / F 24 (step S1), a cell region is extracted from the bright field image (step S2).
  • FIG. 6 shows a detailed flow of the process in step S2.
  • the process of step S2 is executed in cooperation with the control unit 21 and the program stored in the storage unit 25.
  • FIG. 7 shows an extracted image of cell nuclei as an example of the processing in step 2. In the present invention, this is applied to extract a cell region.
  • step S2 first, a bright-field image is converted into a monochrome image (step S201).
  • FIG. 7A shows an example of a bright field image.
  • threshold processing is performed on the monochrome image using a predetermined threshold, and the value of each pixel is binarized (step S202).
  • noise processing is performed (step S203).
  • the noise process can be performed by performing a closing process on the binary image.
  • the closing process is a process in which the contraction process is performed the same number of times after the expansion process is performed.
  • the expansion process is a process of replacing a target pixel with white when at least one pixel in the range of n ⁇ n pixels (n is an integer of 2 or more) from the target pixel is white.
  • the contraction process is a process of replacing a target pixel with black when at least one pixel in the range of n ⁇ n pixels from the target pixel contains black.
  • FIG. 7B shows an example of an image after noise processing. As shown in FIG. 7B, after noise processing, an image (cell image) from which cells are extracted is generated.
  • the labeling process is a process for identifying an object in an image by assigning the same label (number) to connected pixels. By the labeling process, each cell can be identified from the image after the noise process and a label can be applied.
  • step S3 the type of the extracted cell is specified.
  • FIG. 8 shows a detailed flow of the process in step S3.
  • step S301 first, for all cells in the cell image extracted in step S2, from the cell image, the cell area A, the average cell density B, the pixel luminance variation ( ⁇ value) C in the cell region, the cell The “cell feature amount” such as the circularity D and the flatness E of the cell is calculated.
  • the size of the pixel is calculated by measuring the reference length corresponding to the cell image in advance, and the number of pixels in each cell extracted in step S2 is integrated. Thus, the area A of the cell is determined.
  • the average density B of the cells is determined by obtaining the luminance signal value converted into the gray scale of each pixel (pixel) in the cell and calculating the average value.
  • the pixel luminance variation C is determined by calculating the standard deviation of the luminance signal value of each pixel (pixel) in the cell.
  • the circularity D and the flatness E of the cells are determined by applying a constant value obtained from the cell image to the following formulas (d) and (e) for each cell extracted in step S2.
  • (Circularity D) 4 ⁇ S / L2 (d)
  • (Flat ratio E) (ab) / a (e)
  • S represents a cell area (cell area A)
  • “L” represents a cell outer peripheral length.
  • “a” represents the major radius and “b” represents the minor radius.
  • a threshold value process is performed on the cell feature amount obtained in step S301 using a predetermined threshold value, and a cell classification process is performed.
  • a cell classification process is performed.
  • the flatness E value is large
  • the circularity D value is large. Therefore, by setting an appropriate threshold value for identifying these, Classification according to the characteristics of the cell shape can be performed.
  • Each threshold of the cell classification item and the cell feature amount can be set based on the statistical value, and is tabulated in advance and stored in the storage unit 25.
  • the identification of the cell type in step 3 is basically automatically performed in cooperation with the program stored in the control unit 21 and the storage unit 25.
  • auxiliary work by an observer is performed. May be accompanied.
  • the auxiliary work by the observer includes, for example, adjusting each threshold value of the cell feature amount in a stepwise manner with respect to the program stored in the storage unit 25 and visually confirming the specified cell type.
  • Each factor of the cell feature amount (A to E in the above case) may be arbitrarily selected and appropriately changed. Of course, another factor different from the above may be used as a factor of the cell feature amount.
  • step S5 shows a detailed flow of the process in step S5.
  • the process of step S5 is executed in cooperation with the control unit 21 and the program stored in the storage unit 25.
  • step S5 first, a color component corresponding to the wavelength of the fluorescent bright spot is extracted from the fluorescent image (step S501).
  • FIG. 10A shows an example of a fluorescence image.
  • step S501 for example, when the emission wavelength of the fluorescent particles is 550 nm, only the fluorescent bright spot having the wavelength component is extracted as an image.
  • threshold processing is performed on the extracted image, a binarized image is generated, and a bright spot region is extracted (step S502).
  • noise removal processing such as cell autofluorescence and other unnecessary signal components may be performed before the threshold processing, and a low-pass filter such as a Gaussian filter or a high-pass filter such as a second derivative is preferably used.
  • FIG. 10B shows an example of an image from which the bright spot region is extracted. As shown in FIG. 10B, in such an image, a bright spot region centered on the fluorescent bright spot is extracted.
  • step S503 a labeling process is performed on the bright spot area, and a label is assigned to each of the extracted bright spot areas.
  • step S3 and step S5 After the process of step S3 and step S5 is completed, the process returns to the process of FIG. 5 and the addition process of the cell image and the bright spot area image is performed (step S6), and the distribution of the bright spot area on the cell is This is displayed on the display unit 23, and then the biological material expression pattern is classified.
  • the expression level is classified as a biological material expression pattern (step S7).
  • FIG. 11 shows a detailed flow of the process in step S7.
  • step S701 the number of bright spots per cell is calculated based on the cell image and bright spot area image added in step S6.
  • threshold processing is performed on the obtained number of bright spots using a predetermined threshold (step S702), and cell classification processing based on the expression level is performed.
  • Each cell is classified into a plurality of stages according to the expression level.
  • the expression level can be determined based on the number of fluorescent bright spots of PID. That is, since the fluorescent luminescent spots of PID have high luminance and can be detected individually, it is possible to specify the expression level of the biological substance by the number of luminescent spots.
  • the threshold may be set for each type of cell specified in step S3, and the biological material expression level may be classified for each cell type.
  • the classification of biological substance expression levels in step 7 is basically performed automatically in cooperation with the program stored in the control unit 21 and the storage unit 25, but such processing is performed by an observer.
  • Auxiliary work may be involved.
  • the auxiliary work by the observer includes, for example, work for adjusting each threshold value of the expression level of the biological material in stages with respect to the program stored in the storage unit 25.
  • FIG. 12 shows a detailed flow of the process in step S8, and FIG. 13 shows an example of the expression level drawing displayed on the display unit.
  • an information box 231 and a drawing box 232 are displayed on the drawing screen.
  • the information box 231 displays information such as the cell type identified in step S3 and the corresponding biological material expression level threshold
  • the drawing box 232 displays the cell image and bright spot area in step S6.
  • An image to which the images are added hereinafter referred to as a cell distribution image 234) and expression pattern information 233 in which the expression level is expressed as a numerical value are displayed.
  • the threshold value of the expression level for each cell type is displayed in the information box 231 as the biological material expression pattern classification information set in step S7 (step S801).
  • classification is performed in three stages of high, medium and low according to the expression level.
  • the display method of the expression pattern information 233 of the biological material in the drawing box 232 is determined (step S802).
  • the method for displaying the numerical value of the expression level is different for each class of cells classified based on the expression level of the biological material.
  • a numerical value as the expression pattern information 233 is displayed on the cell distribution image 234.
  • the numerical value has a different size, thickness, etc. It is possible to easily grasp the level of expression level by visual recognition.
  • the expression pattern information 233 may be displayed in a color corresponding to the threshold set in step S801, and the level of expression is expressed not only by the expression pattern information 233 but also by the thickness and color of the frame surrounding the cell. May be.
  • numerical colors are classified into four colors of red, yellow, green, and blue, for example, from those having a high expression level.
  • step S804 the coordinates (XY coordinates) of each cell are obtained.
  • the cell coordinates the center coordinates and the vertex coordinates of a rectangle surrounding the cell so as to be in contact with the outer edge of the cell are obtained.
  • center coordinates Sa and Sb and vertex coordinates Va1 to Va4 and Vb1 to Vb4 of rectangles surrounding the cells are calculated, respectively.
  • step S805 the center coordinates of the expression pattern information 233 are set to be the center coordinates of each cell.
  • step S806 the vertex coordinates of the expression pattern information 233 are calculated.
  • the vertex coordinates of the expression pattern information 233 are the coordinates of the rectangular vertices surrounding the numerical value, and are calculated based on the center coordinates and the size of the expression pattern information 233 set in step S802.
  • step S807 it is determined whether or not duplication occurs when the expression pattern information 233 is drawn in the drawing box 232 (step S807).
  • FIG. 14B when the expression pattern information 233a and the expression pattern information 233b are displayed for the cells C1 and C2, duplication may occur. At this time, since it becomes difficult to discriminate because the numerical values overlap, it is necessary to correct the display position so as not to overlap.
  • step S807: Yes the display position of the expression pattern information 233 is corrected (step S808). As a correction method, as shown in FIG.
  • the vertex coordinates of the expression pattern information 233 are moved so as to coincide with the vertices (in this case, Va1 and Vb4) that are sufficiently distant from other cells among the square vertices. Also good.
  • step S807 when the correction of the display position of the expression pattern information 233 is completed, or when it is determined in the step S807 that the expression pattern information 233 is not overlapped with each other (step S807: No), the expression amount drawing process is terminated.
  • Drawing of the expression pattern in step 8 is basically automatically performed in cooperation with the program stored in the control unit 21 and the storage unit 25.
  • auxiliary work by the observer is performed. It may be accompanied.
  • the auxiliary work by the observer is, for example, arbitrarily setting the display method of the expression pattern information 233 in step S802 for the program stored in the storage unit 25, and the display position of the expression pattern information 233 in step S808.
  • the correction method is set and visual confirmation of the corrected expression pattern information 233 is included.
  • the expression pattern of the biological material is quantified using the fluorescent nanoparticles, and the cells are classified according to the expression pattern.
  • the use of fluorescent nanoparticles makes it possible to quantitatively analyze biological materials, which was impossible with the conventional staining method, so that the accuracy of diagnosis can be improved.
  • a display method of expression pattern information is set according to cell classification and displayed together with a cell distribution image. This eliminates the need for the observer to observe the cell distribution and the amount of expression of the biological material while sequentially associating them, and makes it possible to grasp the expression pattern at a glance.
  • the expression pattern information overlaps with each other, rearrange the expression pattern information so that the expression pattern information does not overlap. Furthermore, the expression pattern information is displayed in a color different from the color of the cell distribution image. Thereby, the discriminability of each expression pattern information can be maintained.
  • a tissue section stained with an HE staining reagent is used as the bright field image.
  • a protein specifically expressed in macrophages (hereinafter referred to as a bright field image). It is different in that a tissue section stained with a dye described later is used with a macrophage protein) as a target.
  • the cells forming the microenvironment include stromal cells (fibroblasts, endothelial cells, leukocytes (lymphocytes (B cells, T cell NK cells, T-reg etc.)), monocytes, neutrophils, eosinophils.
  • Spheres, basophils, etc.)) dead cells, glandular cells, fat cells, epithelial cells, etc., and macrophages are classified as monocytes. Furthermore, dyeing using two or more different dyes is also possible, and dyeing combining dye dyeing and H dyeing E dyeing can also be used.
  • Step (A) of staining macrophage protein includes a step (A) of staining macrophage protein, a step (B) of staining the target protein, and a step (C) of quantitatively evaluating a signal derived from the target protein.
  • Steps (A) and (B) are steps performed on the same specimen.
  • the order of the steps (A) and (B) is not particularly limited, but it is usually preferable to carry out the step (A) ⁇ step (B) in that order, and then carry out the step (C).
  • the step (D) is preferably further included, and the steps (D) and (E) are more preferably included.
  • the step (D) is a step of specifying the position and number of macrophages by staining in the step (A).
  • the step (E) specifies information on the expression state of the target protein based on the signal derived from the target protein measured in the step (C) and the position and number of macrophages specified in the step (D). It is a process to do.
  • the information that can be acquired in this embodiment preferably includes information based on the signal derived from the target protein measured in the step (C), and the position and number of macrophages identified in the step (D), and Those based on information on the expression state of the target protein identified in the step (E) are more preferred.
  • the expression amount of the target protein per unit area of the specimen for example, the number of macrophages per unit area of the specimen, the ratio of TAM to the total number of macrophages contained in the specimen, Among the target proteins per unit area, the amount expressed in tumor cells and the amount expressed in macrophages (TAM), and their ratio, the morphology of tissues and cells contained in the specimen, etc. It is possible by staining with more than one type. In particular, it preferably includes at least one of the position and expression level of the target protein in macrophages, and more preferably includes the position and expression level.
  • the staining performed in the step (A) is preferably dye staining
  • the staining performed in the step (B) is preferably fluorescent staining
  • the “signal derived from the target protein” is applied to the fluorescently stained target protein. It is preferably based on the number of derived bright spots.
  • a labeling substance is directly or indirectly bound to a macrophage protein and a target protein to be stained by bringing a specimen and a labeling substance, which will be described later, into contact with each other.
  • immunostaining is preferably performed by reacting a labeled antibody obtained by binding a labeling substance to an antibody that directly or indirectly binds to a macrophage protein or a target protein with a specimen.
  • the macrophage protein stained in the step (A) in the information acquisition method of the present invention can be arbitrarily selected from proteins specifically expressed in macrophages, for example, CD163, CD204, CD68. , Iba1, CD11c, CD206, CD80, CD86, CD163, CD181, CD197, iNOS, Arginase1, CD38, Egr2, etc., in particular, CD68, CD163, and CD204. It is preferable to select from.
  • the macrophage protein is preferably a protein that is specifically expressed in M2 macrophages, and is also preferably a protein that is expressed in tumor-associated macrophages (TAM).
  • TAM tumor-associated macrophages
  • CD163 and CD204 are preferable as proteins specifically expressed in M2 macrophages.
  • dye staining is performed on the macrophage protein.
  • the dye staining is not particularly limited as long as it is a technique for staining macrophage protein with a dye capable of bright-field observation.
  • a labeling substance enzyme
  • the enzyme substrate A method of staining a target substance by depositing a dye on a specimen by adding a dye (substrate) that develops color by reaction is widely used.
  • immunostaining can be performed by adding a dye that is a substrate of the enzyme to a sample that has been reacted in advance with a labeled antibody in which the enzyme is bound to an antibody that binds directly or indirectly to the target protein.
  • a dye that is a substrate of the enzyme
  • the enzyme include peroxidase and alkaline phosphatase
  • the dye include 3,3′-diaminobenzidine (DAB), Histogreen, TMB, Betazoid DAB, Cardassian DAB, Bajoran Purple, VinaGreen, Romulin AEC, Can be mentioned.
  • Target protein The target protein stained in the step (B) in the information acquisition method of the present invention is at least one kind of protein contained in the specimen, and is not particularly limited.
  • the target protein include CSF- Used as a biomarker in pathological diagnosis of colony-stimulating factor receptors such as 1R, PD-L1 (Programmed cell death1 ligand 1), B7-1 / 2, CD8, CD30, CD48, CD59 And a protein involved in immune cell metabolism such as IDO (Indoleamine-2,3-dioxygenase-1).
  • the target protein is preferably a protein (antigen) expressed in macrophages, more preferably a protein specifically expressed in macrophages, and particularly preferably a protein specifically expressed in M2 macrophages.
  • the target protein is preferably a protein expressed in TAM, and more preferably a protein specifically expressed in TAM.
  • Specific target proteins are preferably CSF-1R, IDO, PDL1, B7-1 / 2, CD8, CD30, CD48, and CD59, and more preferably CSF-1R, IDO, or PDL1.
  • CD68 is cited as the macrophage protein and CSF-1R is cited as the target protein
  • CSF-1R is cited as the target protein
  • Example 1 Pre-staining treatment (1-1) Deparaffinization treatment
  • Deparaffinization treatment was performed on lung adenocarcinoma tissue array slides (HLug-Ade150Sur-02: US Biomax) according to the following procedure.
  • the tissue array slide was left in a 65 ° C. incubator for 15 minutes to melt the paraffin in the slide.
  • Each was immersed in three containers containing xylene for 5 minutes, washed with dehydrated ethanol (Kanto Chemical; 14599-95), and further immersed in dehydrated ethanol for 5 minutes x 2 times. Thereafter, it was further dehydrated with 99.5% ethanol (Kanto Chemical; 14033-70) and washed by flowing in pure water for 10 minutes.
  • the deparaffinized tissue array slide is immersed in an activation solution (10 mM Tris buffer (pH 9.0)) preliminarily heated to 95 ° C. and left for 45 minutes. After leaving it to reach room temperature, it is washed by exposing it to pure water flowing for 10 minutes, and the section slide is immersed in a staining vat containing PBS and washed 5 times ⁇ 3 times.
  • an activation solution 10 mM Tris buffer (pH 9.0)
  • a fluorescent image was taken using a fluorescent microscope “BX-53” (Olympus Corporation).
  • the specimen was irradiated with excitation light corresponding to the biotinylated phosphor-aggregated particles used in the fluorescent labeling (3-4) to emit fluorescence, and a stained image in that state was photographed.
  • the wavelength of the excitation light was set to 575 to 600 nm using the excitation light optical filter provided in the fluorescence microscope, and the wavelength of the fluorescence to be observed was set to 612 to 692 nm using the fluorescence optical filter.
  • the intensity of the excitation light at the time of observation and image photographing with a fluorescence microscope was such that the irradiation energy near the center of the visual field was 900 W / cm 2.
  • the exposure time at the time of image shooting was adjusted within a range in which the luminance of the image was not saturated, and was set to, for example, 4000 ⁇ sec.
  • the dye-stained image and the fluorescence image were superimposed and image processing was performed.
  • cells stained with HistGreen that is, cells stained with CD68 were used as macrophages, and samples containing macrophages were extracted.
  • samples containing macrophages bright spots derived from CSF-1R per macrophage cell were further measured. Note that the number of bright spots representing the phosphor-aggregated particles having a luminance of a predetermined value or more was measured.
  • Table 1 shows the number of bright spots per macrophage and macrophage cells contained in the image of the specimen containing macrophages. It can be seen that the number of bright spots contained (the expression level of CSF-1R) differs for each TAM.
  • the operation of acquiring and analyzing the above-described fluorescence image and bright field image is the same as the ⁇ operation of the pathological diagnosis support system 100> in the first embodiment, and thus detailed description thereof will be made. Description is omitted.
  • a dyed staining image performed on the macrophage protein can be obtained by shooting with illumination light applied to the specimen.
  • the step (D) is a step of specifying the position and number of M2 macrophages from the dyed stained image. Is preferable, and a step of specifying the position and number of TAMs is more preferable.
  • the status information can be specified in more detail. Specifically, for example, the average expression level and density of the target protein per macrophage (eg, TAM), the localization of the target protein in the macrophage, and the expression level in the macrophage relative to the total expression level of the target protein per unit area of the specimen. The ratio of the expression level of the target protein.
  • breast cancer subtypes can be classified by expression analysis of hormone receptors (estrogen receptor (ER) and progesterone receptor (PgR)), HER2, and Ki67.
  • hormone receptors estrogen receptor (ER) and progesterone receptor (PgR)
  • PgR progesterone receptor
  • the expression level is classified as the biological material expression pattern, but the present invention is not limited to this.
  • the intracellular distribution and density of the biological material Classification can also be performed by a histogram or a curve represented by the expression level and the number of cells corresponding to the expression level.
  • the following classification method can be mentioned. For example, if HER2 is specifically expressed in the cell membrane, there is a high possibility that it is a cancer cell. Therefore, a threshold value is set for the expression level in the cell membrane, and positive cells or negative cells are classified. Classification can also be performed according to the type of cell or region to which the biological material belongs.
  • the biological material expressed in T cells in order to make it easier to observe the level of expression of biological material in T cells attacking cancer cells, there is a method of displaying the biological material expressed in T cells separately from the biological material expressed in B cells. is there.
  • classification can also be performed according to the distance from a cell or a specific region. For example, by changing the display according to the distance from the edge of the tumor region (portion where cancer cells are gathered), it is possible to easily see how much biological material has infiltrated the tumor region.
  • the cell shape is used as the cell feature amount.
  • the present invention is not limited to this, and the shape of the cell nucleus may be extracted as the cell feature amount.
  • positive cells or negative cells can be classified by detecting atypia such as enlargement of cell nuclei in cancer cells.
  • an HDD or a semiconductor non-volatile memory is used as a computer-readable medium of the program according to the present invention, but the present invention is not limited to this example.
  • a portable recording medium such as a CD-ROM can be applied.
  • a carrier wave carrier wave is also applied as a medium for providing program data according to the present invention via a communication line.
  • the present invention can be used for an image processing apparatus and a program.
  • Image processing device 3A Image processing device 3A Cable 21 control unit (input means, first extraction means, second extraction means, generation means, classification means, display control means, identification means) 22 Operation unit 23 Display unit 231 Information box 232 Drawing box 233 Expression pattern information 234 Cell distribution image 24 Communication I / F 25 storage unit 26 bus 100 pathological diagnosis support system

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Abstract

La présente invention aborde le problème de réalisation d'un dispositif de traitement d'images et d'un programme selon lesquels un motif d'expression d'une substance biologique dans un spécimen de tissu peut être évalué quantitativement, et le niveau d'expression de la substance biologique pour chaque cellule peut facilement être reconnu visuellement. La présente invention comporte un moyen d'entrée destiné à entrer une image de forme représentant la forme d'une cellule et une image de fluorescence dans la même plage que l'image de forme dans laquelle l'expression de la substance biologique est représentée par des points lumineux fluorescents dans un spécimen de tissu dans lequel un type ou une pluralité de types de substances biologiques sont teints, un premier moyen d'extraction destiné à extraire une zone de cellule de l'image de forme, un deuxième moyen d'extraction destiné à extraire une zone de points lumineux fluorescents de l'image fluorescente, un moyen générateur destiné à calculer le niveau d'expression de la substance biologique à partir du nombre de zones de points lumineux fluorescents extraits par le deuxième moyen d'extraction et à générer des informations de motif d'expression (233) incluant le niveau d'expression, et un moyen de classification destiné à séparer des cellules en classes en fonction des informations de motif d'expression générées par le moyen générateur.
PCT/JP2018/003587 2017-02-06 2018-02-02 Dispositif et programme de traitement d'images WO2018143406A1 (fr)

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