# US 12,223,645 B2 · Method and device for identifying abnormal cell in to-be-detected sample, and storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,645 B2 - Document ID: US12223645B2 - Status: Granted - Kind: B2 - Application number: 17/701,910 - Filed: 2022-03-23 - Published: 2022-07-07 - Granted: 2025-02-11 - Earliest claimed: 2020-05-09 - Assignees: Tencent Technology (Shenzhen) Company Limited - Applicants: Tencent Technology (Shenzhen) Company Limited - Inventors: De Cai; Hu Ye; Zhaoxuan Ma; Xiao Han - Web page: https://patentgrep.com/patent/US12223645B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06V 10/809: {of classification results, e.g. where the classifiers operate on the same input data} - G06V 20/698: {Matching; Classification} - G06T 2207/10012: Stereo images - G06T 2207/10056: Microscopic image - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30242: Counting objects in image - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Methods, apparatus, device, and storage medium for identifying an abnormal cell in a to-be-detected sample are disclosed. The method includes obtaining, by a device, multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining, by the device, multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining, by the device, a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining, by the device, a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster; and determining, by the device, whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result. ## Claims ### Claim 1 A method for identifying an abnormal cell in a to-be-detected sample, the method comprising: obtaining, by a device comprising a memory storing instructions and a processor in communication with the memory, multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining, by the device, multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining, by the device, a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining, by the device, a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster, including: selecting at least one layer of image blocks from the multi-layer image blocks of the cell cluster, and inputting the at least one layer of image blocks into a first part of the second image identification network to obtain a second single-layer identification result corresponding to the multi-layer image blocks of the cell cluster; and inputting the multi-layer image blocks of the cell cluster into a second part of the second image identification network to obtain a second multi-layer identification result corresponding to the multi-layer image blocks of the cell cluster; and determining, by the device, whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result. ### Claim 2 (depends on 1) The method according to claim 1, wherein the obtaining the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster according to the multi-layer images comprises: dividing the multi-layer images into a plurality of multi-layer image blocks; and classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster. ### Claim 3 (depends on 2) The method according to claim 2, wherein the classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster comprises: performing the classification by determining a quantity of cell nuclei in each of the plurality of multi-layer image blocks. ### Claim 4 (depends on 1) The method according to claim 1, wherein the obtaining the first identification result by the first image identification network according to the multi-layer image blocks of the single cell comprises: selecting at least one layer of image blocks from the multi-layer image blocks of the single cell, and inputting the at least one layer of image blocks into a first part of the first image identification network to obtain a first single-layer identification result corresponding to the multi-layer image blocks of the single cell; inputting the multi-layer image blocks of the single cell into a second part of the first image identification network to obtain a first multi-layer identification result corresponding to the multi-layer image blocks of the single cell; and fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result, wherein: the first part of the first image identification network is used for extracting plane information of the single cell, and the second part of the first image identification network is used for extracting three-dimensional structure information of the single cell. ### Claim 5 (depends on 4) The method according to claim 4, wherein the fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result comprises: performing weighted averaging on the first single-layer identification result and the first multi-layer identification result for a plurality of cell types of the single cell, to obtain the first identification result. ### Claim 6 (depends on 1) The method according to claim 1, wherein the obtaining the second identification result by the second image identification network according to the multi-layer image blocks of the cell cluster further comprises: fusing the second single-layer identification result and the second multi-layer identification result to obtain the second identification result, wherein: the first part of the second image identification network is used for extracting plane information of the cell cluster, and the second part of the second image identification network is used for extracting three-dimensional structure information of the cell cluster. ### Claim 7 (depends on 1) The method according to claim 1, wherein: the first image identification network is trained based on a first data set, and the first data set comprises multi-layer images of the single cell and labels of cell types of the multi-layer images of the single cell; and the second image identification network is trained based on a second data set, and the second data set comprises multi-layer images of the cell cluster and labels of cell types of the multi-layer images of the cell cluster. ### Claim 8 An apparatus for identifying an abnormal cell in a to-be-detected sample, the apparatus comprising: a memory storing instructions; and a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to perform: obtaining multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster, obtaining multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images, obtaining a first identification result by a first image identification network according to the multi-layer image blocks of the single cell, obtaining a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster, including: selecting at least one layer of image blocks from the multi-layer image blocks of the cell cluster, and inputting the at least one layer of image blocks into a first part of the second image identification network to obtain a second single-layer identification result corresponding to the multi-layer image blocks of the cell cluster; and inputting the multi-layer image blocks of the cell cluster into a second part of the second image identification network to obtain a second multi-layer identification result corresponding to the multi-layer image blocks of the cell cluster, and determining whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result. ### Claim 9 (depends on 8) The apparatus according to claim 8, wherein the obtaining the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster according to the multi-layer images comprises: dividing the multi-layer images into a plurality of multi-layer image blocks; and classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster. ### Claim 10 (depends on 9) The apparatus according to claim 9, wherein the classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster comprises: performing the classification by determining a quantity of cell nuclei in each of the plurality of multi-layer image blocks. ### Claim 11 (depends on 8) The apparatus according to claim 8, wherein the obtaining the first identification result by the first image identification network according to the multi-layer image blocks of the single cell comprises: selecting at least one layer of image blocks from the multi-layer image blocks of the single cell, and inputting the at least one layer of image blocks into a first part of the first image identification network to obtain a first single-layer identification result corresponding to the multi-layer image blocks of the single cell; inputting the multi-layer image blocks of the single cell into a second part of the first image identification network to obtain a first multi-layer identification result corresponding to the multi-layer image blocks of the single cell; and fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result, wherein: the first part of the first image identification network is used for extracting plane information of the single cell, and the second part of the first image identification network is used for extracting three-dimensional structure information of the single cell. ### Claim 12 (depends on 11) The apparatus according to claim 11, wherein the fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result comprises: performing weighted averaging on the first single-layer identification result and the first multi-layer identification result for a plurality of cell types of the single cell, to obtain the first identification result. ### Claim 13 (depends on 8) The apparatus according to claim 8, wherein the obtaining the second identification result by the second image identification network according to the multi-layer image blocks of the cell cluster further comprises: fusing the second single-layer identification result and the second multi-layer identification result to obtain the second identification result, wherein: the first part of the second image identification network is used for extracting plane information of the cell cluster, and the second part of the second image identification network is used for extracting three-dimensional structure information of the cell cluster. ### Claim 14 (depends on 8) The apparatus according to claim 8, wherein: the first image identification network is trained based on a first data set, and the first data set comprises multi-layer images of the single cell and labels of cell types of the multi-layer images of the single cell; and the second image identification network is trained based on a second data set, and the second data set comprises multi-layer images of the cell cluster and labels of cell types of the multi-layer images of the cell cluster. ### Claim 15 A non-transitory computer-readable storage medium, storing computer-readable instructions, wherein, the computer-readable instructions, when executed by a processor, are configured to cause the processor to perform: obtaining multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster, including: selecting at least one layer of image blocks from the multi-layer image blocks of the cell cluster, and inputting the at least one layer of image blocks into a first part of the second image identification network to obtain a second single-layer identification result corresponding to the multi-layer image blocks of the cell cluster; and inputting the multi-layer image blocks of the cell cluster into a second part of the second image identification network to obtain a second multi-layer identification result corresponding to the multi-layer image blocks of the cell cluster; and determining whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result. ### Claim 16 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the obtaining the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster according to the multi-layer images comprises: dividing the multi-layer images into a plurality of multi-layer image blocks; and classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium according to claim 16, wherein the classifying the plurality of multi-layer image blocks into the multi-layer image blocks of the single cell and the multi-layer image blocks of the cell cluster comprises: performing the classification by determining a quantity of cell nuclei in each of the plurality of multi-layer image blocks. ### Claim 18 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the obtaining the first identification result by the first image identification network according to the multi-layer image blocks of the single cell comprises: selecting at least one layer of image blocks from the multi-layer image blocks of the single cell, and inputting the at least one layer of image blocks into a first part of the first image identification network to obtain a first single-layer identification result corresponding to the multi-layer image blocks of the single cell; inputting the multi-layer image blocks of the single cell into a second part of the first image identification network to obtain a first multi-layer identification result corresponding to the multi-layer image blocks of the single cell; and fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result, wherein: the first part of the first image identification network is used for extracting plane information of the single cell, and the second part of the first image identification network is used for extracting three-dimensional structure information of the single cell. ### Claim 19 (depends on 18) The non-transitory computer-readable storage medium according to claim 18, wherein the fusing the first single-layer identification result and the first multi-layer identification result to obtain the first identification result comprises: performing weighted averaging on the first single-layer identification result and the first multi-layer identification result for a plurality of cell types of the single cell, to obtain the first identification result. ### Claim 20 (depends on 15) The non-transitory computer-readable storage medium according to claim 15, wherein the obtaining the second identification result by the second image identification network according to the multi-layer image blocks of the cell cluster further comprises: fusing the second single-layer identification result and the second multi-layer identification result to obtain the second identification result, wherein: the first part of the second image identification network is used for extracting plane information of the cell cluster, and the second part of the second image identification network is used for extracting three-dimensional structure information of the cell cluster.