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WO2015198620A1 - Procédé de cartographie tissulaire - Google Patents

Procédé de cartographie tissulaire Download PDF

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Publication number
WO2015198620A1
WO2015198620A1 PCT/JP2015/053506 JP2015053506W WO2015198620A1 WO 2015198620 A1 WO2015198620 A1 WO 2015198620A1 JP 2015053506 W JP2015053506 W JP 2015053506W WO 2015198620 A1 WO2015198620 A1 WO 2015198620A1
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fragment
fragments
gene
section
feature
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Japanese (ja)
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森本　伸彦
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Olympus Corp
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Olympus Corp
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • 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

Definitions

  • the present invention relates to a tissue map creation method, and more particularly to a method of creating a map showing a two-dimensional distribution of a cell population having a disease-related gene in a pathological section of a living tissue.
  • Patent Documents 1 and 2 Conventionally, as a method for analyzing gene distribution in a section cut out from a biological tissue, a method in which the section is directly subjected to molecular biological analysis has been proposed (for example, see Patent Documents 1 and 2).
  • Patent Document 1 a section is divided into a plurality of fragments, and the individual fragments obtained are subjected to genetic analysis.
  • Patent Document 2 cuts brain tissue into strips, obtains gene expression data for each of the obtained sections, and calculates gene expression data obtained over multiple layers, thereby calculating various genes in the brain tissue. The three-dimensional distribution map is reconstructed.
  • methods for subjecting sections directly to molecular biological analysis include immunohistochemistry (IHC) method represented by immunostaining method, in situ hybridization (ISH) method, and fluorescence ISH method.
  • IHC immunohistochemistry
  • ISH in situ hybridization
  • fluorescence ISH method fluorescence ISH
  • Non-Patent Document 1 It is known that multiple types of cell populations with different genetic characteristics coexist in a diseased tissue such as a tumor tissue, and what kind of genetic population is distributed and how There is a need for a technique for accurately analyzing whether or not.
  • the present invention has been made in view of the above-described circumstances, and it is possible to create a tissue map that can understand at a glance the two-dimensional distribution of a cell population having a specific genetic characteristic in a section of a living tissue.
  • An object is to provide a method for creating an organization map.
  • the present invention relates to a section dividing step for dividing a section of biological tissue into a plurality of fragments, a gene analysis step for performing molecular biological analysis on a plurality of genes included in each of the plurality of fragments, and the gene analysis step
  • a feature amount calculation step for obtaining a plurality of feature amounts for each of the fragments by calculating a feature amount from each of the obtained analysis results of the plurality of genes, and the plurality of features obtained in the feature amount calculation step
  • a fragment classification step for classifying the plurality of fragments into one or more groups based on the similarity between the quantities, and a different display mode for each of the one or more groups is set.
  • Providing an organization map creation method including an organization map creation step of creating an organization map in which a section corresponding to a position in the map is displayed in a display mode corresponding to a group to which the fragment belongs That.
  • the section is divided into a plurality of fragments in the section dividing step, and the molecular biological analysis of each of the plurality of fragments is performed on the plurality of genes in the gene analyzing step. Then, in the feature amount calculation step, a feature amount indicating each analysis result of each fragment is calculated, and in the fragment classification step, a plurality of fragments are added so that fragments having the same or similar feature amount belong to the same group. Classify. Thereby, fragments having the same or similar analysis results for a plurality of genes, that is, fragments containing the same type of cell population are classified into the same group.
  • the same display mode is given to the section corresponding to the position of the fragment containing the same type of cell population, and the other display mode is given to the section corresponding to the position of the fragment containing the other type of cell population.
  • an organization map is created. In this tissue map, it is possible to understand at a glance the two-dimensional distribution of a cell population having a specific genetic characteristic within a section of a living tissue.
  • the plurality of fragments may be classified by cluster analysis based on the plurality of feature amounts of the fragments. In this way, even if the number of fragments and the number of types of genes to be analyzed are large, a plurality of fragments can be classified with high accuracy.
  • a threshold value is set for each of the plurality of feature amounts, and the plurality of fragments are classified based on the plurality of feature amounts and the threshold value of each of the fragments. May be.
  • two or more threshold values may be set for each of the plurality of feature amounts. By doing so, at least one feature amount differs between the two types of cell populations. Therefore, when two types of cell populations coexist on one fragment, at least one feature amount is present. , A weighted average of the number of cells in both cell populations, ie an intermediate value. Therefore, by setting two threshold values, it is possible to specify a fragment having an intermediate feature value, that is, a fragment corresponding to the boundary between two types of cell populations. In addition, for the boundaries of three or more types of cell populations, fragments corresponding to the boundaries can be obtained by using the same method as in the case of the two types, or by combining the classification results of the surrounding fragments and the continuity of the boundaries. Can be specified.
  • FIG. 5A It is a figure explaining another example of the organization map creation process of Drawing 3, and shows the organization map created from the section of Drawing 5A based on the scatter diagram of Drawing 5B. It is a figure explaining the classification
  • FIG. 6B shows a matrix showing the Hamming distance between the fragments of the section of FIG. 6A. 6A shows a tabulation table relating to the Hamming distance of each fragment in FIG. 6A.
  • the tissue map creation method is a tissue map showing the distribution of various cell populations contained in tissue sections of tissue collected from a living body, particularly the two-dimensional distribution of cell populations having specific gene mutations related to diseases. Is to create.
  • tissue map showing the distribution of a cell population having a cancer-related mutant gene in a pathological section created from a tumor tissue will be described.
  • the tissue map creation method is executed by, for example, a system including a gene analysis device and an information processing device such as a computer that creates a tissue map by performing arithmetic processing on the analysis value obtained by the gene analysis device.
  • a section dividing step SA1 for dividing a section into a plurality of fragments, and each of the plurality of fragments obtained in the section dividing step SA1 is subjected to molecular biological analysis.
  • Gene analysis step SA2 a feature amount calculation step SA3 for calculating the feature amount of each fragment based on the analysis result obtained in the gene analysis step SA2, and a similarity between the feature amounts obtained in the feature amount calculation step SA3
  • Fragment classification step SA4 for classifying a plurality of fragments based on the degree
  • organization map creation for determining the display mode of each section in the white map based on the classification result obtained in the fragment classification step SA4 and creating an organization map Step SA5 is included.
  • the pathological section is divided into a plurality of fragments, and each fragment is separately collected in a container such as a microtube.
  • a cell sorting method using a cell sorting system described in International Publication No. 2011/149909 is suitably used for dividing a pathological section and collecting fragments.
  • each fragment of each of two or more types of cancer-related genes or cancer-related gene-related substances (hereinafter collectively referred to simply as cancer-related genes) is subjected to a molecular biological technique.
  • the molecular biological technique is, for example, frequency analysis of base sequence reading results by a next-generation sequencer or analysis by a microarray.
  • Analysis items include, for example, gene mutation rate, mRNA and microRNA expression level, genomic DNA methylation rate, gene amplification rate and deletion rate, presence or absence of fusion genes, gene analysis combined with immunological techniques Acetylation and methylation of histone proteins to be analyzed. Specific contents of the analysis items and the analysis method will be described later.
  • the next feature quantity calculation step SA3, fragment classification step SA4, and tissue map creation step SA5 are executed by the information processing device based on the analysis values received from the genetic analysis device.
  • a feature amount is calculated from the analysis result of each cancer-related gene obtained from the fragment.
  • the characteristic amount may be an analysis value obtained by molecular biological analysis, for example, a numerical value itself such as a gene mutation rate, a methylation rate, an amplification factor, and a fluorescence intensity indicating an expression level, and may be used for statistical processing, etc. It may be a numerical value obtained by performing post-processing on the analysis value.
  • a fragment classification step SA4 for each fragment, a feature amount vector composed of a plurality of types of feature amounts obtained in the feature amount calculation step SA3 is calculated, and a feature vector of one fragment and a feature of another fragment are calculated. A distance between the vectors is calculated, and the feature vectors are subjected to cluster analysis based on the calculated distance. Thereby, a plurality of fragments are classified so that fragments having the same or similar feature vectors belong to the same group.
  • the distance between feature vectors for example, Euclidean distance, Manhattan distance, Pearson correlation coefficient, etc. are used.
  • an unsupervised learning method or a supervised learning method may be used. Examples of unsupervised learning methods include hierarchical clustering, k-means method, and self-organizing map method.
  • the supervised learning method information on a standard fragment feature vector and a group into which the fragment is classified is given in advance. Supervised learning is suitable for diagnosis based on gene analysis results and classification of fragments having certain conditions.
  • the organization map creation step SA5 different display modes are set for each group generated in the fragment classification step SA4. Thereby, one display mode is given to each fragment.
  • the display mode is, for example, a color hue, saturation and brightness, or a pattern such as hatching.
  • the display mode of each section in the white map is associated with the display mode of the corresponding fragment, and the associated display mode is set to each section. To give.
  • the white map has a plurality of sections 5 separated by boundary lines 4 corresponding to the dividing lines 2 of the pathological section 1. Each section 5 corresponds to one piece 3.
  • the organization map 10 is created by giving the display mode of the fragment 3 corresponding to each section 5 to the section 5.
  • a total of 43 fragments 3 are classified into three groups I, II, and III, and sections where no analysis results exist are displayed as blanks.
  • fragment 3 is group I in which both gene X and Y mutations are positive, group II and gene in which only gene X mutations are positive. It is classified into group III in which only the Y mutation was positive.
  • the created organizational map is displayed on the computer display or output to paper or electronic file.
  • a tissue map When outputting a tissue map to a computer display or electronic file, for example, when an image of a pathological section before division is stored and a section in the tissue map is designated using an input device such as a mouse The image of the corresponding fragment may be called up and displayed.
  • the created organization map fragments having the same or similar feature vectors are displayed in the same display mode.
  • the feature vector indicates the presence and type of gene mutation in the cell population within the fragment, and the feature vectors of the fragments that obtained the same or similar analysis results for multiple cancer-related genes are the same or similar. It will be a thing. That is, in the tissue map, the region where the same type of cell population having the same type of genetic variation is distributed is displayed in the same display mode, and the region where the other type of cell population having the different type of genetic variation is distributed Are displayed in other display modes. Therefore, it can be understood from the tissue map at a glance how the cell population having the gene mutation is distributed in the pathological section. Further, by acquiring an image of a pathological section before division and comparing the image of the pathological section with a tissue map, the relationship between the tissue morphology and gene mutation can also be confirmed.
  • the tissue map creation method according to the present embodiment includes a segment division step SB1, a gene analysis step SB2, a feature amount calculation step SB3, a fragment classification step SB4, SB5, and a tissue map creation step. SB6.
  • the steps SB1, SB2, SB3, and SB6 are the same as the segment division step SA1, the gene analysis step SA2, the feature amount calculation step SA3, and the tissue map creation step SA5 described in the first embodiment, respectively.
  • the fragment classification steps SB4 and SB5 are different from those of the first embodiment. Therefore, in the present embodiment, the fragment classification steps SB4 and SB5 are mainly described, and the descriptions of the other steps SB1, SB2, SB3, and SB6 are omitted.
  • N ⁇ 1 threshold values are set for each of a plurality of types of feature amounts obtained in the feature amount calculation step SB3 (step SB4).
  • set N groups being separated by thresholds if cancer-related genes to be analyzed is M types, N M number of groups is set .
  • each fragment is classified into a group corresponding to the feature amount (step SB5).
  • a plurality of fragments the fragment between the same or similar analytical results were obtained for a plurality of cancer-related genes to belong to the same group are classified into N M number of groups at the maximum.
  • the threshold value may be set to the same value for all types of feature values, or may be set individually for each feature value. If the pathological section contains many stromal cells, the analysis value obtained by the molecular biological analysis is small and the feature amount is small, so the threshold value is set to a small value.
  • the threshold value may be set based on histograms of various feature amounts. For example, when the histogram of the feature quantity is multimodal having a plurality of peaks, it is preferable to set a threshold value in the vicinity of the minimum point between two adjacent peaks. Furthermore, there are gene DNAs that are difficult to detect depending on the sequence, and when a plurality of genes are detected, there are cases where the detection results are not reliable. The feature amount derived from such an analysis result may not be used in classification.
  • one threshold ⁇ is set for the mutation rate of gene X, and the mutation rate of gene Y is determined.
  • One threshold value ⁇ is set.
  • the threshold values ⁇ and ⁇ are set to 0.8, for example.
  • the plurality of fragments are classified into four groups XY ⁇ , X + Y +, X + Y ⁇ , and XY + based on the mutation rates of the genes X and Y.
  • Group XY- is a group in which both gene X and Y mutations are negative
  • group X + Y + is a group in which both genes X and Y mutations are positive
  • group X + Y- is a gene X mutation only
  • the positive group, group XY +, is a group in which only the mutation of gene Y is positive.
  • two threshold values ⁇ 1, ⁇ 2 are set for the mutation rate of gene X
  • two threshold values ⁇ 1, ⁇ 2 are set for the mutation rate of gene Y.
  • ⁇ 2 ( ⁇ 1 ⁇ 2) may be set.
  • the smaller threshold values ⁇ 1 and ⁇ 1 are set to 0.4, for example, and the larger threshold values ⁇ 2 and ⁇ 2 are set to 0.6, for example.
  • the plurality of fragments are classified into five groups XY ⁇ , X + Y +, X + Y ⁇ , XY +, and Boundary based on the mutation rates of the genes X and Y.
  • the group Boundary is a group corresponding to a boundary between one type of cell population and another one type of cell population.
  • FIG. 4C shows an example of a tissue map of the pathological section shown in FIG. 4A created using the classification method shown in FIG. 4B.
  • FIG. 5C shows an example of a tissue map of the pathological section of FIG. 5A created using the classification method shown in FIG. 5A.
  • fragments having the same or similar combination of feature quantities are displayed in the same display mode. That is, in the tissue map, the region where the same type of cell population having the same type of genetic variation is distributed is displayed in the same display mode, and the region where the other type of cell population having the different type of genetic variation is distributed Are displayed in other display modes. Therefore, it can be understood from the tissue map at a glance how the cell population having the gene mutation is distributed in the pathological section. Further, by acquiring an image of a pathological section before division and comparing the image of the pathological section with a tissue map, the relationship between the tissue morphology and gene mutation can also be confirmed.
  • At least one of the mutation rates of the plurality of types of genes is often about 0.5. Therefore, by setting two threshold values so as to sandwich 0.5 with respect to the feature amount, as shown in FIG. 5B, so that a fragment having a feature amount of 0.5 and its vicinity is extracted.
  • a fragment that is a boundary between a plurality of types of cell populations ie, Boundary in FIG. 5C can be identified.
  • an image of a pathological section before division is stored, and when one section in a tissue map is designated using an input device such as a mouse, the corresponding fragment image is called up and displayed. It may be like this.
  • the threshold value can be changed even after the organization map is created. When the threshold value is changed, the steps SB4, SB5, and SB6 are executed again to re-classify the plurality of fragments and the organization map. May be recreated. At this time, a fragment once classified as a boundary may be always displayed as a boundary.
  • a feature vector and a Hamming distance may be calculated as follows, and a plurality of fragments may be classified based on the Hamming distance. That is, by setting N ⁇ 1 threshold values for various feature amounts, the various feature amounts are converted into N values. A feature vector composed of this N-valued feature quantity is calculated for each fragment. Next, a Hamming distance between feature vectors is calculated, and fragments whose Hamming distance is equal to or less than a predetermined threshold are classified into the same group. This method is particularly useful when the number of cancer-related genes to be analyzed and the number of analysis items are large.
  • FIG. 6A to FIG. 6C show an example of a classification method using the Hamming distance.
  • a pathological section is divided into 6 fragments, and the mutation rates of genes W, X, Y, and Z contained in the genomic DNA extracted from each fragment are determined as a next-generation sequencer or the like. Analyze with. Then, the mutation rate of each gene W, X, Y, Z is used as a feature amount, and the threshold for the feature amount is set to 0.5. As a result, the feature quantity of 0 or more and less than 0.5 is binarized to “0”, and the feature quantity of 0.5 to 1 is binarized to “1”.
  • the Hamming distance between one feature vector and another feature vector is obtained. Specifically, the values of the same component of two feature vectors are compared, and when the values are different, 1 is set, and when the values are the same, 0 is set, and the sum of the comparison results of all the components is the feature vector. Hamming distance between. In this example, the minimum value of the Hamming distance is 0 and the maximum value is 4. The more similar the feature vectors, the smaller the Hamming distance.
  • FIG. 6B shows the Hamming distance between feature vectors in a matrix format.
  • FIG. 6C shows the sum, average, and shortest distance of the Hamming distance between each fragment and other fragments.
  • 6 fragments are classified based on the Hamming distance.
  • (1) Select a set of fragments whose Hamming distance is 0. Thereby, three sets (A2, B1), (A2, B2), and (B1, B2) are selected.
  • (2) Among the groups selected in (1) groups including overlapping fragments are grouped into one group. Thereby, one group of (A2, B1, B2) is obtained.
  • (3) Select a set of fragments with a Hamming distance of 1. Thus, two sets (A1, A3) and (A3, B3) are selected.
  • the mutation rate of cancer-related genes is useful for evaluating the malignancy of cancer and selecting therapeutic agents.
  • the mutation rate of genomic DNA and mitochondrial DNA may be analyzed.
  • Gene mutations include fusion gene formation by chromosomal translocation (eg, EML4-ALK mutation in lung cancer and Philadelphia chromosome in some leukemias), deletion and insertion of partial gene sequences, 1 base unit As well as mutations in multiple base units, deletions and insertions, and the like.
  • Such gene mutation rate is measured by frequency analysis of base sequence reading results by a next-generation sequencer (NGS), quantitative analysis by quantitative PCR (qPCR), or quantitative analysis by digital PCR (dPCR).
  • NGS next-generation sequencer
  • qPCR quantitative analysis by quantitative PCR
  • dPCR digital PCR
  • the amplification efficiency is equivalent in genome sequences without mutation on the same gene or in PCR amplification. Quantify the DNA without insertions, deletions, or amplifications in the region. Thereby, the total DNA amount of the cell population contained in one fragment is estimated.
  • the mutation rate of the cancer-related gene is determined as a ratio of the amount of mutated DNA of the cancer-related gene to the estimated total DNA amount.
  • the read frequency that includes mutations and the read frequency that does not include mutations can be obtained from the group of read sequences.
  • the mutation rate of a gene can be obtained by determining the proportion of genome reads containing mutations.
  • the mutation rate of the gene is as close to 1 as possible, or as close to 0 as possible. Further, when the fragment includes a boundary between two cell populations, the mutation rate is 0 depending on the ratio of the number of cells in one cell population to the number of cells in the other cell population. A value between 1 and 1.
  • Gene methylation rate It is known that the methylation rate of genomic DNA is decreased in a tumor suppressor gene and a transcriptional control region of an oncogene in cancer cells.
  • cytosine When cytosine is methylated into a methylated cytosine in the gene transcription region, it becomes difficult for a transcription factor to bind to the gene transcription region, and gene expression is suppressed.
  • CIMP CpG island methylator phenotype
  • the methylation rate of cytosine in the transcription control region is measured by bisulfite treatment of the base sequence of the CpG island in the transcription control region of the gene, and then analyzing the base sequence using a next-generation sequencer or a pyrosequencer (Pyrosequencer). can do. Since the methylated cytosine in the transcription control region treated with bisulfite is converted to uracil, the position of cytosine originally changes to thymine in the reading result of the gene base sequence. The rate of change from cytosine to thymine is the methylation rate, and the methylation rate is a value of 0 or more and 1 or less. Alternatively, the methylation rate may be estimated by qPCR using MSP (Methylation Specific PCR).
  • genomic DNA fragments containing histones that have been modified by methylation or acetylation are collected by, for example, chromatin immunoprecipitation, and the collected genomic fragments are analyzed using a microarray or next-generation sequencer.
  • a method for detecting whether or not histones in the transcription control region are modified is used.
  • Gene amplification rate Gene amplification and deletion (copy number variants) is known as a significant increase or decrease in the copy number of cancer-related genes in tumor cells.
  • genes on an autosome are inherited by one copy each from a paternal and a maternal in one genome. Further, it is known that even in normal cells, two or more copies of a gene may be present.
  • the amplification rate and deletion rate of a gene are measured by frequency analysis of base sequence reading results by a next-generation sequencer or analysis by a microarray.
  • the number of reads of the gene to be analyzed is divided by the number of reads of the gene region considered to be preserved in both normal cells and tumor cells in the fragment. Thereby, amplification magnification is obtained. Furthermore, by measuring the amplification amount of the same gene for all fragments and normalizing the amplification amount of each fragment with the maximum value of the amplification amount of all fragments being 1, an amplification factor of 0 or more and 1 or less is obtained. It is done.
  • the portion of the gene sequence to be amplified is quantified, the DNA sequence in the region where the copy number is conserved is quantified, and the amplification factor is calculated by dividing the former quantitative value by the latter quantitative value.
  • An amplification factor of 0 or more and 1 or less can also be obtained by normalizing the amplification factor of each fragment by setting the maximum value among the amplification factors of fragments to 1.
  • Such a normalization method can be used because normal cells and tumor cells are derived from the same genome.
  • the standard for normalization is that of genes that exist on the same chromosome as the gene to be analyzed and that are likely to be preserved even after tumor formation (for example, sequences that are not involved in cell proliferation, normalization, apoptosis, etc.) The number of leads is used. At this time, the same staining sequence as the gene to be analyzed is selected for normalization because there is a possibility that the number of chromosomes has changed in the tumor cells.

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Abstract

L'invention concerne un procédé de cartographie tissulaire comprenant : une étape de division de section (SA1) pour diviser une section d'un tissu biologique en une pluralité de fragments ; un étape d'analyse de gène (SA2) pour analyser par biologie moléculaire une pluralité de gènes contenus dans chaque fragment ; une étape de calcul de quantité de caractéristiques (SA3) pour calculer des quantités de caractéristiques à partir des résultats d'analyse sur les gènes individuels et acquérir ainsi une pluralité de quantités de caractéristiques pour chaque fragment ; une étape de classification de fragments (SA4) pour classer les fragments dans un ou plusieurs groupes sur base de la similarité parmi des quantités de caractéristiques correspondante ; et une étape de cartographie tissulaire (SA5) pour régler des modes d'affichage pour les groupes respectifs, lesdits modes d'affichage différant les uns des autres, et préparer une carte tissulaire, la position de chaque fragment et une région correspondante dans la section étant affichées dans un mode d'affichage attribué au groupe auquel le fragment appartient.
PCT/JP2015/053506 2014-06-23 2015-02-09 Procédé de cartographie tissulaire Ceased WO2015198620A1 (fr)

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WO2021261185A1 (fr) * 2020-06-26 2021-12-30 ソニーグループ株式会社 Dispositif de traitement d'image, procédé de traitement d'image, programme de traitement d'image et système d'aide au diagnostic

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