Nouritrophoblast cell characteristic analysis method and electronic equipment

Abstract

The invention provides a trophoblast cell characteristic analysis method and electronic equipment, and relates to the technical field of embryo evaluation. The method comprises the following steps: carrying out space quadrant division on a target blastocyst three-dimensional model obtained by three-dimensional reconstruction to obtain a plurality of sub-quadrants; determining polar trophoblast cell information and wall trophoblast cell information according to the internal cell mass information in each sub-quadrant; determining multiple fusion feature data according to the trophoblast cell information, the polar trophoblast cell information and the wall trophoblast cell information in each sub-quadrant; and according to the fusion feature data and the basic feature data, carrying out embryo development result analysis to obtain estimated development result information of the target blastocyst. According to the scheme, the spatial structure features of the trophoblast cells are fully mined to obtain the fusion features, and the embryonic development result can be analyzed more deeply based on the fusion features, so that the accuracy of embryonic development result evaluation is improved, and clinical selection of embryos with higher development potential is assisted.

Description

Technical Field

[0001] The present application relates to the technical field of embryo assessment, and in particular to a method and electronic device for analyzing characteristics of trophoblast cells. Background Art

[0002] With the development of three-dimensional reconstruction technology, the spatial structure of the blastocyst can be accurately simulated through three-dimensional reconstruction technology. Therefore, based on the reconstructed three-dimensional model of the blastocyst, various features used for embryo evaluation can be observed and identified from a three-dimensional perspective.

[0003] At present, the analysis of trophoblast cell characteristics only stays at the level of analysis of basic characteristics such as the number of trophoblast cells and cell density. The types of characteristics are relatively limited, resulting in low accuracy of embryo evaluation. Summary of the invention

[0004] The purpose of the present application is to provide a trophoblast cell feature analysis method and electronic device to address the deficiencies in the above-mentioned prior art, so as to improve the accuracy of embryo development result evaluation through deep feature mining.

[0005] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, the present application provides a method for analyzing characteristics of trophoblast cells, comprising:

[0007] Dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain a plurality of sub-quadrants;

[0008] According to the inner cell mass information in each sub-quadrant, the polar trophoblast cell information and the wall trophoblast cell information are determined; the polar trophoblast cell information includes: the number of polar trophoblast cells; the wall trophoblast cell information includes: the number of trophoblast cells in each designated sub-quadrant, and the designated sub-quadrant is a quadrant that does not contain the polar trophoblast cell;

[0009] Determining a plurality of fusion feature data according to the trophoblast cell information in each sub-quadrant, the polar trophoblast cell information, and the mural trophoblast cell information;

[0010] According to the fusion feature data and basic feature data, the embryo development results are analyzed to obtain the estimated development result information of the target blastocyst.

[0011] Optionally, the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction is divided into spatial quadrants to obtain a plurality of sub-quadrants, including:

[0012] Taking the center of gravity of the target blastocyst three-dimensional model as the origin, the target blastocyst three-dimensional model is divided along the first preset direction, the second preset direction and the third preset direction to obtain the multiple sub-quadrants.

[0013] Optionally, determining the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant includes:

[0014] According to the information of the inner cell mass in each sub-quadrant, the target sub-quadrant where the center of gravity of the inner cell mass is located is determined;

[0015] The polar trophoblast cell information and the parietal trophoblast cell information are determined according to the target sub-quadrant.

[0016] Optionally, determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant includes:

[0017] Determining the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant;

[0018] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

[0019] Optionally, determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant includes:

[0020] Determining the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in each adjacent quadrant of the target sub-quadrant;

[0021] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant and each adjacent quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

[0022] Optionally, the determining of a plurality of fusion feature data according to the trophoblast cell information in each sub-quadrant, the polar trophoblast cell information and the wall trophoblast cell information includes:

[0023] According to the number of trophoblast cells in each sub-quadrant, the heterogeneity characteristic data of trophoblast cells are determined;

[0024] According to the polar trophoblast cell information and the parietal trophoblast cell information, respectively determining mean distribution difference characteristic data and mean difference characteristic data of the polar trophoblast cells and the parietal trophoblast cells;

[0025] Determining the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information;

[0026] The heterogeneity characteristic data of the trophoblast cells, the mean distribution difference characteristic data, the mean difference characteristic data and the cell proportion are respectively used as a fusion characteristic data.

[0027] Optionally, determining the heterogeneity characteristic data of trophoblast cells according to the number of trophoblast cells in each sub-quadrant includes:

[0028] According to the number of trophoblast cells in each sub-quadrant, the standard deviation of the number of trophoblast cells in each sub-quadrant is determined to obtain the heterogeneity characteristic data of the trophoblast cells.

[0029] Optionally, the determining, according to the polar trophoblast cell information and the wall trophoblast cell information, respectively the mean distribution difference characteristic data and the mean difference characteristic data of the polar trophoblast cells and the wall trophoblast cells comprises:

[0030] Based on the number of trophoblast cells in each designated subquadrant, the median number of parietal trophoblast cells was determined;

[0031] Determining the standard deviation between the number of the polar trophoblast cells and the median of the parietal trophoblast cells to obtain the mean distribution difference characteristic data;

[0032] The difference between the number of the polar trophoblast cells and the median number of the parietal trophoblast cells is determined to obtain the mean difference feature data.

[0033] Optionally, determining the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information includes:

[0034] Based on the number of trophoblast cells in each designated subquadrant, the number of mural trophoblast cells was determined;

[0035] The ratio between the number of the parietal trophoblast cells and the number of the polar trophoblast cells is determined to obtain the cell ratio.

[0036] In a second aspect, an embodiment of the present application further provides a trophoblast cell characteristic analysis device, comprising: a processing module, a determination module, and an analysis module;

[0037] The processing module is used to divide the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain a plurality of sub-quadrants;

[0038] The determination module is used to determine the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant; the polar trophoblast cell information includes: the number of polar trophoblast cells; the wall trophoblast cell information includes: the number of trophoblast cells in each designated sub-quadrant, and the designated sub-quadrant is a quadrant that does not contain the polar trophoblast cell;

[0039] The determination module is used to determine a plurality of fusion feature data according to the trophoblast cell information in each sub-quadrant, the polar trophoblast cell information and the wall trophoblast cell information;

[0040] The analysis module is used to analyze the embryo development results based on the fusion feature data and the basic feature data to obtain the estimated development result information of the target blastocyst.

[0041] Optionally, the processing module is specifically used to divide the target blastocyst three-dimensional model along a first preset direction, a second preset direction and a third preset direction with the center of gravity of the target blastocyst three-dimensional model as the origin to obtain the multiple sub-quadrants.

[0042] Optionally, the determination module is specifically configured to determine the target sub-quadrant where the center of gravity of the inner cell mass is located based on the inner cell mass information in each sub-quadrant;

[0043] The polar trophoblast cell information and the parietal trophoblast cell information are determined according to the target sub-quadrant.

[0044] Optionally, the determination module is specifically configured to determine the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant;

[0045] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

[0046] Optionally, the determination module is specifically configured to determine the extreme trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in each adjacent quadrant of the target sub-quadrant;

[0047] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant and each adjacent quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

[0048] Optionally, the determination module is specifically used to determine the heterogeneity characteristic data of trophoblast cells according to the number of trophoblast cells in each sub-quadrant;

[0049] According to the polar trophoblast cell information and the parietal trophoblast cell information, respectively determining mean distribution difference characteristic data and mean difference characteristic data of the polar trophoblast cells and the parietal trophoblast cells;

[0050] Determining the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information;

[0051] The heterogeneity characteristic data of the trophoblast cells, the mean distribution difference characteristic data, the mean difference characteristic data and the cell proportion are respectively used as a fusion characteristic data.

[0052] Optionally, the determination module is specifically configured to determine the standard deviation of the number of trophoblast cells in each sub-quadrant according to the number of trophoblast cells in each sub-quadrant, so as to obtain the heterogeneity characteristic data of the trophoblast cells.

[0053] Optionally, the determination module is specifically configured to determine the median number of parietal trophoblast cells based on the number of trophoblast cells in each designated sub-quadrant;

[0054] Determining the standard deviation between the number of the polar trophoblast cells and the median of the parietal trophoblast cells to obtain the mean distribution difference characteristic data;

[0055] The difference between the number of the polar trophoblast cells and the median number of the parietal trophoblast cells is determined to obtain the mean difference feature data.

[0056] Optionally, the determination module is specifically configured to determine the number of mural trophoblast cells based on the number of trophoblast cells in each designated sub-quadrant;

[0057] The ratio between the number of the parietal trophoblast cells and the number of the polar trophoblast cells is determined to obtain the cell ratio.

[0058] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to implement the trophoblast cell characteristic analysis method provided in the first aspect.

[0059] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for analyzing trophoblast cell characteristics as provided in the first aspect is executed.

[0060] The beneficial effects of this application are:

[0061] The present application provides a method and electronic device for analyzing trophoblast cell characteristics, including: dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain multiple sub-quadrants; determining polar trophoblast cell information and wall trophoblast cell information based on the inner cell mass information in each sub-quadrant; determining multiple fusion feature data based on the trophoblast cell information, polar trophoblast cell information and wall trophoblast cell information in each sub-quadrant; analyzing the embryo development results based on the fusion feature data and basic feature data to obtain the estimated development result information of the target blastocyst. This solution divides the three-dimensional model of the blastocyst into quadrants, so that the polar trophoblast cell information and wall trophoblast cell information can be extracted based on the trophoblast cells in each sub-quadrant, so as to achieve the measurement of polar trophoblast cells and wall trophoblast cells, so that the polar trophoblast cells and wall trophoblast cells can have statistical significance. Afterwards, based on the extracted polar trophoblast cell information and wall trophoblast cell information combined with the trophoblast cell information, the spatial structural characteristics of the trophoblast cells can be fully mined, and multiple fused feature data can be extracted. Based on the multiple fused feature data and the basic feature data, the blastocyst development results can be evaluated. Since the multiple fused feature data are obtained after fully mining the spatial structural characteristics of the trophoblast cells, compared with the traditional embryo development evaluation based on two-dimensional features, the embryo development results can be analyzed more deeply, thereby improving the accuracy of the embryo development result evaluation and assisting the clinic in selecting embryos with greater developmental potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A schematic diagram of a process for analyzing trophoblast cell characteristics provided in an embodiment of the present application;

[0064] Figure 2 A schematic diagram of quadrant division of a three-dimensional model of a target blastocyst provided in an embodiment of the present application;

[0065] Figure 3 A schematic diagram of another method for analyzing trophoblast cell characteristics provided in an embodiment of the present application;

[0066] Figure 4 A schematic diagram of a process for analyzing characteristics of another trophoblast cell provided in an embodiment of the present application;

[0067] Figure 5A schematic diagram of another method for analyzing trophoblast cell characteristics provided in an embodiment of the present application;

[0068] Figure 6 A schematic diagram of another method for analyzing trophoblast cell characteristics provided in an embodiment of the present application;

[0069] Figure 7 A schematic diagram of another method for analyzing trophoblast cell characteristics provided in an embodiment of the present application;

[0070] Figure 8 A schematic diagram of a process for analyzing characteristics of another trophoblast cell provided in an embodiment of the present application;

[0071] Fig. 9 A schematic diagram of a trophoblast cell characteristic analysis device provided in an embodiment of the present application;

[0072] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0073] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0074] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0075] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0076] With the rapid development of computer vision and assisted reproductive technology, the application of artificial intelligence in the field of assisted reproduction is becoming increasingly in-depth. How to simulate the spatial structure of the blastocyst through three-dimensional reconstruction technology without damaging the embryo, thereby revealing information that cannot be observed from a two-dimensional perspective, has always been one of the focuses of embryology experts.

[0077] Since its introduction into the field of embryo culture, time-difference culture technology has brought about tremendous changes in assisted reproductive technology, subverted the traditional working mode of embryologists, and greatly improved work efficiency. With the help of time-difference culture technology, not only can more embryo characteristics be obtained, but it also facilitates the formation of a variety of embryo scoring systems. In addition, the mature application of time-difference culture technology in the field of assisted reproduction has laid a solid foundation for non-invasive three-dimensional reconstruction of embryos. Through the embryo three-dimensional reconstruction technology based on time-difference culture imaging data, the three-dimensional spatial structure of the blastocyst can be accurately simulated, providing a new way to deeply explore the characteristics of the blastocyst stage.

[0078] Trophoblast cells, as an important component of blastocysts, are the key to ensuring the smooth development of embryos. However, in clinical practice, the measurement dimension of trophoblast cells is limited to the number of trophoblast cells. Although embryologists have further discovered that trophoblast cells can be divided into polar trophoblast cells and parietal trophoblast cells based on their functionality, this set of concepts has not been truly used in clinical practice due to the limitations of technology. With the maturity of three-dimensional reconstruction technology and its implementation in the field of assisted reproduction, this concept can be further measured and its role in clinical practice can be evaluated.

[0079] This scheme aims to divide the three-dimensional model of the blastocyst into quadrants based on the three-dimensional model obtained after three-dimensional reconstruction, so as to extract trophoblast cells based on the divided quadrants, and combine the definitions of polar trophoblast cells, wall trophoblast cells and at least one fusion feature to extract multiple fusion features that can characterize the spatial structural characteristics of trophoblast cells, thereby assisting embryologists to have a deeper understanding of the differentiation process of trophoblast cells and inner cell mass, and assisting clinical selection of high-quality embryos with greater developmental potential.

[0080] Polar Trophectoderm (pTE) cells are part of the trophectoderm and are located next to the inner cell mass (ICM). These cells are in close contact with the inner cell mass in the blastocyst. Polar trophoblast cells have a higher potential for division and are part of the future placenta. Together with the inner cell mass, they participate in the process of embryo implantation and placental development.

[0081] Mural trophoblast cells (mTE) are another part of the trophectoderm in the blastocyst, located opposite to the inner cell mass and close to the blastocyst cavity. The mTE cells are generally considered to be a less active part of the trophectoderm, which undergo cell differentiation and eventually participate in the formation of extraembryonic embryonic tissues (placenta).

[0082] Figure 1 A schematic diagram of a method for analyzing trophoblast cell characteristics provided in an embodiment of the present application; the execution subject of this solution may be a computer device, such as Figure 1 As shown, the method may include:

[0083] S101, dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain a plurality of sub-quadrants.

[0084] Optionally, multi-focal plane images of the target blastocyst can be acquired based on the time-difference culture dish, and based on the acquired multi-focal plane images, three-dimensional reconstruction technology is used to perform three-dimensional reconstruction of the target blastocyst to obtain a three-dimensional model of the target blastocyst.

[0085] Next, the three-dimensional model of the target blastocyst is spatially divided into quadrants to obtain multiple sub-quadrants. That is, through quadrant division, the three-dimensional model of the target blastocyst can be divided into multiple sub-quadrants, and each sub-quadrant can include part of the cell tissue of the target blastocyst.

[0086] S102. Determine polar trophoblast cell information and parietal trophoblast cell information according to the inner cell mass information in each sub-quadrant.

[0087] The polar trophoblast cell information includes: the number of polar trophoblast cells; the parietal trophoblast cell information includes: the number of trophoblast cells in each designated sub-quadrant, and the designated sub-quadrant is a quadrant that does not contain polar trophoblast cells.

[0088] Since the pole trophoblast cells have a certain positional relationship with the inner cell mass, the pole trophoblast cell information can be determined based on the inner cell mass information in each sub-quadrant, and then based on the determined pole trophoblast cell information, the parietal trophoblast cell information can be further determined.

[0089] In this embodiment, the polar trophoblast cell information may include the number of polar trophoblast cells, and the parietal trophoblast cell information may refer to the number of trophoblast cells in each sub-quadrant that does not include polar trophoblast cells.

[0090] S103. Determine a plurality of fusion feature data according to the trophoblast cell information, the polar trophoblast cell information, and the wall trophoblast cell information in each sub-quadrant.

[0091] It is worth noting that, in this embodiment, polar trophoblast cells and parietal trophoblast cells can be defined based on trophoblast cells, that is, the number of polar trophoblast cells and the number of parietal trophoblast cells can be obtained based on trophoblast cell statistics, but trophoblast cells are not equivalent to polar trophoblast cells and parietal trophoblast cells.

[0092] The trophoblast cell information in each sub-quadrant and the polar trophoblast cell information and the wall trophoblast cell information determined above can be combined to calculate multiple fusion feature data according to the calculation strategy of each fusion feature.

[0093] The reason why they are called fusion features here is that these features cannot be directly extracted from the target blastocyst three-dimensional model, but need to be obtained through certain feature mining and calculation.

[0094] S104. Analyze the embryo development results based on the fused feature data and the basic feature data to obtain the estimated development result information of the target blastocyst.

[0095] Optionally, the basic feature data may refer to features that can be directly extracted from the three-dimensional model of the target blastocyst. In this embodiment, the basic feature data may include the total number of trophoblast cells contained in the target blastocyst.

[0096] Combining the various fusion feature data and basic feature data obtained above, the target blastocyst can be evaluated for development based on the correlation between the fusion features and basic features and the embryo development results obtained in advance through statistical analysis, and the estimated development result information of the target blastocyst can be obtained.

[0097] It should be noted that in the stage of studying the correlation between characteristics and embryonic development results, a three-dimensional model of each sample blastocyst can be obtained, and the fusion feature data of each sample blastocyst can be extracted according to the above steps. Then, the fusion feature data and basic feature data of each sample blastocyst can be combined to perform data statistical analysis to obtain the correlation between the fusion features and basic features and the embryonic development results.

[0098] In summary, the trophoblast cell feature analysis method provided in this embodiment includes: dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain multiple sub-quadrants; determining the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant; determining multiple fusion feature data according to the trophoblast cell information, polar trophoblast cell information and wall trophoblast cell information in each sub-quadrant; analyzing the embryo development results according to the fusion feature data and the basic feature data to obtain the estimated development result information of the target blastocyst. This scheme divides the three-dimensional model of the blastocyst into quadrants, so that the polar trophoblast cell information and the wall trophoblast cell information can be extracted based on the trophoblast cells in each sub-quadrant, so as to achieve the measurement of the polar trophoblast cells and the wall trophoblast cells, so that the polar trophoblast cells and the wall trophoblast cells can have statistical significance. Afterwards, based on the extracted polar trophoblast cell information and wall trophoblast cell information combined with the trophoblast cell information, the spatial structural characteristics of the trophoblast cells can be fully mined, and multiple fused feature data can be extracted. Based on the multiple fused feature data and the basic feature data, the blastocyst development results can be evaluated. Since the multiple fused feature data are obtained after fully mining the spatial structural characteristics of the trophoblast cells, compared with the traditional embryo development evaluation based on two-dimensional features, the embryo development results can be analyzed more deeply, thereby improving the accuracy of the embryo development result evaluation and assisting the clinic in selecting embryos with greater developmental potential.

[0099] Optionally, in step S101, the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction is spatially divided into quadrants to obtain multiple sub-quadrants, which may include: taking the center of gravity of the three-dimensional model of the target blastocyst as the origin, and dividing the three-dimensional model of the target blastocyst into multiple sub-quadrants along a first preset direction, a second preset direction, and a third preset direction respectively.

[0100] Typically, when performing quadrant division, it can be divided into 8 quadrants, 6 quadrants or 4 quadrants. In this embodiment, the target blastocyst three-dimensional model can be divided into 8 sub-quadrants. This division method can improve the accuracy of subsequent extraction of fusion features.

[0101] Figure 2 A schematic diagram of the quadrant division of a target blastocyst three-dimensional model provided in an embodiment of the present application, such as Figure 2 As shown, the target blastocyst 3D model is divided into 8 sub-quadrants. The target blastocyst 3D model may be divided into 8 sub-quadrants by taking the center of gravity of the target blastocyst 3D model as the origin and dividing the target blastocyst 3D model into quadrants along the x direction, y direction and z direction as shown in the figure.

[0102] Figure 3A schematic flow chart of another trophoblast cell feature analysis method provided in an embodiment of the present application; Optionally, in step S102, determining the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant may include:

[0103] S301. Determine the target sub-quadrant where the center of gravity of the inner cell mass is located according to the inner cell mass information in each sub-quadrant.

[0104] Based on the divided sub-quadrants, the inner cell mass can be identified, the position of the inner cell mass can be determined, and the sub-quadrant where the center of gravity of the inner cell mass is located can be used as the target sub-quadrant.

[0105] S302. Determine polar trophoblast cell information and parietal trophoblast cell information according to the target sub-quadrant.

[0106] Since there is a certain spatial position relationship between the polar trophoblast cells and the parietal trophoblast cells and the inner cell mass, the polar trophoblast cell information and the parietal trophoblast cell information can be determined separately according to the target sub-quadrant where the center of gravity of the inner cell mass is located.

[0107] Figure 4 A schematic flow chart of another trophoblast cell feature analysis method provided in an embodiment of the present application; Optionally, in step S302, determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant may include:

[0108] S401. Determine polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant.

[0109] Since the polar trophoblast cells are in close contact with the inner cell mass in the blastocyst, in one feasible manner, the trophoblast cells in the sub-quadrant where the center of gravity of the inner cell mass is located can be used as the polar trophoblast cells. Therefore, the number of polar trophoblast cells can be determined based on the number of trophoblast cells in the target sub-quadrant to obtain the polar trophoblast cell information.

[0110] S402, determining the number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant, and obtaining the wall trophoblast cell information; each designated sub-quadrant is the remaining sub-quadrants.

[0111] In this way, the remaining sub-quadrants except the target sub-quadrant are all regarded as a designated sub-quadrant, and the mural trophoblast cell information can be obtained according to the number of trophoblast cells in the remaining sub-quadrants.

[0112] In other words, the mural trophoblast cell information does not refer to the number of mural trophoblast cells, but the number of trophoblast cells in each designated sub-quadrant is collectively referred to as the mural trophoblast cell information.

[0113] Exemplarily, the target subquadrant is subquadrant 1, and the remaining subquadrants are subquadrant 2, subquadrant 3, subquadrant 4, subquadrant 5, subquadrant 6, subquadrant 7, and subquadrant 8; assuming that the number of trophoblast cells contained in subquadrant 1 is 15; the number of trophoblast cells contained in subquadrant 2, subquadrant 3, subquadrant 4, subquadrant 5, subquadrant 6, subquadrant 7, and subquadrant 8 are 10, 12, 17, 22, 19, 14, and 32, respectively.

[0114] The determined polar trophoblast cell information can be: the number of polar trophoblast cells is 15; the parietal trophoblast cell information can be: the number of trophoblast cells in subquadrant 2 is 10, the number of trophoblast cells in subquadrant 3 is 12, the number of trophoblast cells in subquadrant 4 is 17, the number of trophoblast cells in subquadrant 5 is 22, the number of trophoblast cells in subquadrant 6 is 19, the number of trophoblast cells in subquadrant 7 is 14, and the number of trophoblast cells in subquadrant 8 is 32.

[0115] Figure 5 A schematic flow chart of another trophoblast cell feature analysis method provided in an embodiment of the present application; Optionally, in step S302, determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant may include:

[0116] S501. Determine the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in each adjacent quadrant of the target sub-quadrant.

[0117] In another achievable manner, the trophoblast cells in the sub-quadrant where the center of gravity of the inner cell mass is located and the trophoblast cells in the adjacent quadrants of the sub-quadrant can be used as the extreme trophoblast cells. Therefore, the number of extreme trophoblast cells can be calculated based on the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in the adjacent sub-quadrants of the target sub-quadrant, thereby obtaining the extreme trophoblast cell information.

[0118] S502, determining the number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant and each adjacent quadrant, and obtaining the wall trophoblast cell information; each designated sub-quadrant is the remaining sub-quadrant.

[0119] In this manner, the remaining sub-quadrants except the target sub-quadrant and the sub-quadrants adjacent to the target sub-quadrant are all used as a designated sub-quadrant. The mural trophoblast cell information can be obtained according to the number of trophoblast cells in each of the remaining sub-quadrants.

[0120] Combination Figure 2 , assuming that the target sub-quadrant is sub-quadrant 2, the adjacent sub-quadrants of the target sub-quadrant are: sub-quadrant 1, sub-quadrant 4 and sub-quadrant 6.

[0121] Figure 6A flow chart of another trophoblast cell feature analysis method provided in an embodiment of the present application; Optionally, in step S103, multiple fusion feature data are determined based on the trophoblast cell information, the polar trophoblast cell information, and the wall trophoblast cell information in each sub-quadrant, which may include:

[0122] S601. Determine heterogeneity characteristic data of trophoblast cells according to the number of trophoblast cells in each sub-quadrant.

[0123] Based on the divided three-dimensional model of the target blastocyst, the number of trophoblast cells in each sub-quadrant can be extracted, and the heterogeneity of trophoblast cells can be obtained by calculating the variance, standard deviation or coefficient of variation. Heterogeneity is used to characterize the uniformity of the distribution of trophoblast cells in three-dimensional space.

[0124] S602. Determine the mean distribution difference characteristic data and mean difference characteristic data of the polar trophoblast cells and the parietal trophoblast cells respectively according to the polar trophoblast cell information and the parietal trophoblast cell information.

[0125] According to the number of polar trophoblast cells and the median or mean of the number of trophoblast cells in the remaining sub-quadrants, by calculating the standard deviation or coefficient of variation, the characteristic data of the mean distribution difference between polar trophoblast cells and parietal trophoblast cells can be obtained.

[0126] According to the number of polar trophoblast cells and the median or average number of trophoblast cells in the remaining sub-quadrants, by calculating the difference, the mean difference characteristic data between polar trophoblast cells and parietal trophoblast cells can be obtained.

[0127] S603. Determine the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information.

[0128] In this embodiment, the number of parietal trophoblast cells is first calculated based on the number of trophoblast cells in the remaining sub-quadrants, that is, the number of trophoblast cells in the remaining sub-quadrants is added to obtain the number of parietal trophoblast cells, and then the cell ratio between parietal trophoblast cells and polar trophoblast cells is obtained based on the number of polar trophoblast cells and the number of parietal trophoblast cells. The cell ratio is used to measure the differences within the blastocyst.

[0129] S604. The heterogeneity characteristic data, mean distribution difference characteristic data, mean difference characteristic data and cell proportion of the trophoblast cells are respectively taken as a fusion characteristic data.

[0130] Optionally, the heterogeneity feature data, mean distribution difference feature data, mean difference feature data and cell proportion calculated above are respectively used as a fusion feature data.

[0131] In practical applications, feature mining can also be performed from different angles to generate more fused feature data with greater research significance.

[0132] Optionally, in step S601, determining the heterogeneity characteristic data of trophoblast cells according to the number of trophoblast cells in each sub-quadrant may include: determining the standard deviation of the number of trophoblast cells in each sub-quadrant according to the number of trophoblast cells in each sub-quadrant to obtain the heterogeneity characteristic data of trophoblast cells.

[0133] In one implementation, the standard deviation of the number of trophoblast cells in each sub-quadrant may be calculated, and the calculation result may be used as heterogeneity characteristic data. Each sub-quadrant here may refer to the eight sub-quadrants obtained by division, and the sub-quadrant containing the polar trophoblast cells is not excluded.

[0134] In other implementations, the variance or coefficient of variation of the number of trophoblast cells in each sub-quadrant may be calculated, and the variance or coefficient of variation may be used as heterogeneity feature data.

[0135] It is worth noting that the smaller the heterogeneity characteristic data is, the more uniform the distribution of trophoblast cells in three-dimensional space is.

[0136] Figure 7 A flow chart of another trophoblast cell feature analysis method provided in an embodiment of the present application; Optionally, in step S602, according to the polar trophoblast cell information and the wall trophoblast cell information, respectively determining the mean distribution difference feature data and the mean difference feature data of the polar trophoblast cells and the wall trophoblast cells may include:

[0137] S701. Determine the median number of parietal trophoblast cells according to the number of trophoblast cells in each designated sub-quadrant.

[0138] The median number of trophoblast cells in each designated sub-quadrant can be determined based on the number of trophoblast cells in each designated sub-quadrant, and the median number is used as the median number of mural trophoblast cells.

[0139] S702. Determine the standard deviation between the number of polar trophoblast cells and the median of parietal trophoblast cells to obtain mean distribution difference characteristic data.

[0140] By calculating the standard deviation between the number of polar trophoblast cells and the median number of parietal trophoblast cells, the mean distribution difference characteristic data can be obtained.

[0141] S703. Determine the difference between the number of polar trophoblast cells and the median of parietal trophoblast cells to obtain mean difference feature data.

[0142] By calculating the difference between the number of polar trophoblast cells and the median number of parietal trophoblast cells, mean difference feature data can be obtained.

[0143] Of course, the above is only one possible calculation method. In actual applications, the average number of trophoblast cells in each designated sub-quadrant can also be calculated.

[0144] The standard deviation between the number of polar trophoblast cells and the average number of parietal trophoblast cells is used as the mean distribution difference characteristic data; or the coefficient of variation between the number of polar trophoblast cells and the median of parietal trophoblast cells is used as the mean distribution difference characteristic data; or the coefficient of variation between the number of polar trophoblast cells and the average number of parietal trophoblast cells is used as the mean distribution difference characteristic data.

[0145] In addition, the difference between the number of polar trophoblast cells and the average number of parietal trophoblast cells can be used as mean difference feature data.

[0146] The coefficient of variation is calculated as follows: standard deviation / mean value*100%; the coefficient of variation is used to measure the degree of variation.

[0147] Figure 8 A flow chart of another method for analyzing trophoblast cell characteristics provided in an embodiment of the present application; Optionally, in step S603, determining the cell ratio between the wall trophoblast cells and the pole trophoblast cells according to the pole trophoblast cell information and the wall trophoblast cell information may include:

[0148] S801. Determine the number of mural trophoblast cells according to the number of trophoblast cells in each designated sub-quadrant.

[0149] First, the number of trophoblast cells in each designated sub-quadrant can be added to obtain the number of parietal trophoblast cells. It is worth noting that when the polar trophoblast cells are obtained from the trophoblast cells in the sub-quadrant where the center of gravity of the inner cell mass is located, the designated sub-quadrants here are 7 sub-quadrants; when the polar trophoblast cells are obtained from the trophoblast cells in the sub-quadrant where the center of gravity of the inner cell mass is located and the adjacent sub-quadrants, the designated sub-quadrants here are 4 sub-quadrants.

[0150] S802. Determine the ratio between the number of parietal trophoblast cells and the number of polar trophoblast cells to obtain a cell ratio.

[0151] Optionally, the ratio between the number of parietal trophoblast cells and the number of polar trophoblast cells can be used as the above-mentioned cell ratio.

[0152] Before evaluating the development of the target blastocyst based on the various fusion feature data calculated above, the results of the correlation analysis between the various fusion feature data and the basic feature data and the embryo development results are briefly explained here:

[0153] By performing three-dimensional reconstruction on the collected sample blastocyst and obtaining a three-dimensional model of the sample blastocyst, the basic characteristic data of the sample blastocyst can be extracted, including but not limited to the volume of the sample blastocyst, the volume of the inner cell mass, the number of trophoblast cells, etc. According to each basic characteristic data, the blastocyst characteristic data range obtained in the experiment is used to clean and screen each basic characteristic data, and blastocysts that are too large or too small are deleted from the sample blastocyst.

[0154] For the remaining sample blastocysts, the fusion feature data corresponding to each sample blastocyst can be extracted in the above manner, and then the clinical development outcomes of the embryos are analyzed based on the fusion feature data and basic feature data corresponding to each sample blastocyst. The results are roughly as follows:

[0155] (1) Trophoblast heterogeneity: Cross-analysis with trophoblast cell counts found that blastocysts with trophoblast cell counts greater than 95 and more uniform heterogeneity distribution (heterogeneity less than 1.7) had better clinical development outcomes, while among blastocysts with trophoblast cell counts less than 95, embryos with heterogeneity less than 1.7 had higher pregnancy rates and live birth rates. The specific results are shown in Tables 1, 2, and 3, respectively:

[0156] Table 1

[0157]

[0158] Table 2

[0159] Clinical live birth rate Trophoblast cell count <= 95 Trophoblast count > 95 Heterogeneity of TE distribution <1.7 49.43％(172 / 348) 56.07％(420 / 749) Heterogeneity of TE distribution>=1.7 40.71％(103 / 253) 55.11％(372 / 675)

[0160] Table 3

[0161] Clinical miscarriage rate Trophoblast cell count <= 95 Trophoblast count > 95 Heterogeneity of TE distribution <1.7 15.69％(32 / 204) 17.00％(86 / 506) Heterogeneity of TE distribution>=1.7 20.77％(27 / 130) 15.84％(70 / 442)

[0162] (2) Extreme trophoblast cell number: The ROC (Receiver Operating Characteristic) method was used to find the optimal threshold. Through cross-analysis with the trophoblast cell number, it can be seen that blastocysts with trophoblast cell numbers greater than 95 and blastocysts with extreme trophoblast cell numbers greater than 12 have greater developmental potential. Among blastocysts with trophoblast cell numbers less than 95, blastocysts with extreme trophoblast cell numbers greater than 12 have higher clinical pregnancy rates and live birth rates. The specific results are shown in Tables 4, 5 and 6 below:

[0163] Table 4

[0164] Clinical pregnancy rate TE cell count <95 TE cell number>=95 Extreme trophoblast cell count <12 49.47％(140 / 283) 66.39％(322 / 485) The number of trophoblast cells>=12 58.51％(110 / 188) 66.42％(710 / 1069)

[0165] Table 5

[0166] Clinical live birth rate TE cell count <95 TE cell number>=95 Extreme trophoblast cell count <12 41.70％(118 / 283) 58.14％(282 / 485) The number of trophoblast cells>=12 48.40％(91 / 188) 53.88％(576 / 1069)

[0167] Table 6

[0168] Clinical miscarriage rate TE cell count <95 TE cell number>=95 Extreme trophoblast cell count <12 15.71％(22 / 140) 12.42％(40 / 322) The number of trophoblast cells>=12 17.27％(19 / 110) 18.87％(134 / 710)

[0169] Among them, ROC can obtain different true positive rates (TPR) and false positive rates (FPR) by changing the threshold, thereby drawing the ROC curve. The horizontal axis of the ROC curve is FPR, and the vertical axis is TPR. Generally speaking, as the threshold increases, TPR decreases, while FPR increases. Different points on the ROC curve correspond to different thresholds. According to the specific requirements of the task, you can choose a suitable threshold.

[0170] (3) Difference in the mean distribution of polar trophoblast cells and parietal trophoblast cells: The index was classified into two categories using the median. If the difference in the mean distribution of polar trophoblast cells and parietal trophoblast cells was greater than or equal to 0.7, the probability of clinical pregnancy increased. The specific results are shown in Table 7:

[0171] Table 7

[0172] No clinical pregnancy Clinical pregnancy The difference in distribution between trophoblast and mural trophoblast means is < 0.7 41.87％(175) 58.13％(243) The distribution difference between the mean of trophoblast cells and mural trophoblast cells is >= 0.7 35.35％(568) 64.65％(1039)

[0173] (4) Difference in the mean of polar trophoblast cells and parietal trophoblast cells: This indicator is divided into three categories: the number of polar trophoblast cells is greater than the median of parietal trophoblast cells, the number of polar trophoblast cells is less than the median of parietal trophoblast cells, and the number of polar trophoblast cells is equal to the median of arm trophoblast cells. Through analysis, it was found that blastocysts with a number of polar trophoblast cells greater than the median of parietal trophoblast cells had a higher clinical pregnancy rate, but were also more likely to miscarry. The specific results are shown in Tables 8 and 9:

[0174] Table 8

[0175] No clinical pregnancy Clinical pregnancy The number of polar trophoblast cells is less than the median number of parietal trophoblast cells 36.00％(288) 64.00％(512) The number of polar trophoblast cells is equal to the median number of parietal trophoblast cells 41.87％(175) 58.13％(243) The number of polar trophoblast cells is greater than the median number of parietal trophoblast cells 34.70％(280) 65.30％(527)

[0176] Table 9

[0177] Live birth abortion The number of polar trophoblast cells is less than the median number of parietal trophoblast cells 85.71％(439) 14.26％(73) The number of polar trophoblast cells is equal to the median number of parietal trophoblast cells 86.83％(211) 13.17％(32) The number of polar trophoblast cells is greater than the median number of parietal trophoblast cells 79.13％(417) 20.87％(110)

[0178] (5) Cell ratio of wall trophoblast cells and polar trophoblast cells: The ROC method was used to find the optimal threshold. Through cross-analysis with the number of trophoblast cells, it can be seen that the clinical pregnancy rate and live birth rate of blastocysts with a trophoblast cell number greater than 95 and a cell ratio less than 7 are higher. The specific results are shown in Tables 10 and 11:

[0179] Table 10

[0180] Clinical pregnancy rate TE cell number <= 95 TE cell count>95 Cell percentage <7 58.50％(172 / 294) 66.76％(458 / 686) Cell ratio>=7 52.77％(162 / 307) 66.40％(490 / 738)

[0181] Table 11

[0182] Clinical live birth rate TE cell number <= 95 TE cell count>95 Cell percentage <7 45.92％(135 / 294) 53.06％(364 / 686) Cell ratio>=7 45.60％(140 / 307) 57.99％(428 / 738)

[0183] Optionally, based on the above analysis results of the correlation between the fusion characteristics and basic characteristics and the clinical development outcome of the embryo, the developmental outcome of the target blastocyst can be estimated based on the obtained fusion characteristic data and basic characteristic data of the target blastocyst to determine whether the target blastocyst has developmental potential. The basic characteristic data here can be the number of trophoblast cells.

[0184] In summary, the trophoblast cell feature analysis method provided in this embodiment includes: dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain multiple sub-quadrants; determining the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant; determining multiple fusion feature data according to the trophoblast cell information, polar trophoblast cell information and wall trophoblast cell information in each sub-quadrant; analyzing the embryo development results according to the fusion feature data and the basic feature data to obtain the estimated development result information of the target blastocyst. This scheme divides the three-dimensional model of the blastocyst into quadrants, so that the polar trophoblast cell information and the wall trophoblast cell information can be extracted based on the trophoblast cells in each sub-quadrant, so as to achieve the measurement of the polar trophoblast cells and the wall trophoblast cells, so that the polar trophoblast cells and the wall trophoblast cells can have statistical significance. Afterwards, based on the extracted polar trophoblast cell information and wall trophoblast cell information combined with the trophoblast cell information, the spatial structural characteristics of the trophoblast cells can be fully mined, and multiple fused feature data can be extracted. Based on the multiple fused feature data and the basic feature data, the blastocyst development results can be evaluated. Since the multiple fused feature data are obtained after fully mining the spatial structural characteristics of the trophoblast cells, compared with the traditional embryo development evaluation based on two-dimensional features, the embryo development results can be analyzed more deeply, thereby improving the accuracy of the embryo development result evaluation and assisting the clinic in selecting embryos with greater developmental potential.

[0185] The following describes the apparatus, equipment, storage medium, etc. used to perform the trophoblast cell characteristic analysis method provided in the present application. The specific implementation process and technical effects are described above and will not be repeated below.

[0186] Fig. 9 A schematic diagram of a trophoblast cell characteristic analysis device provided in an embodiment of the present application, the functions implemented by the trophoblast cell characteristic analysis device correspond to the steps performed by the above method. The device can be understood as the above-mentioned computer device or server, or the processor of the server, or can also be understood as a component independent of the above-mentioned server or processor that implements the functions of the present application under the control of the server, such as Fig. 9 As shown, the device may include: a processing module 910, a determination module 920, and an analysis module 930;

[0187] A processing module 910 is used to divide the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain a plurality of sub-quadrants;

[0188] A determination module 920 is used to determine polar trophoblast cell information and parietal trophoblast cell information according to the inner cell mass information in each sub-quadrant; the polar trophoblast cell information includes: the number of polar trophoblast cells; the parietal trophoblast cell information includes: the number of trophoblast cells in each designated sub-quadrant, and the designated sub-quadrant is a quadrant that does not contain polar trophoblast cells;

[0189] A determination module 920, for determining a plurality of fusion feature data according to the trophoblast cell information, the polar trophoblast cell information and the wall trophoblast cell information in each sub-quadrant;

[0190] The analysis module 930 is used to analyze the embryo development results based on the fused feature data and the basic feature data to obtain the estimated development result information of the target blastocyst.

[0191] Optionally, the processing module 910 is specifically used to divide the target blastocyst three-dimensional model along a first preset direction, a second preset direction and a third preset direction with the center of gravity of the target blastocyst three-dimensional model as the origin to obtain multiple sub-quadrants.

[0192] Optionally, the determination module 920 is specifically configured to determine the target sub-quadrant where the center of gravity of the inner cell mass is located according to the inner cell mass information in each sub-quadrant;

[0193] Based on the target subquadrant, polar trophoblast cell information as well as parietal trophoblast cell information was determined.

[0194] Optionally, the determination module 920 is specifically configured to determine the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant;

[0195] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant is determined to obtain the wall trophoblast cell information; each designated sub-quadrant is the remaining sub-quadrants.

[0196] Optionally, the determination module 920 is specifically configured to determine the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in each adjacent quadrant of the target sub-quadrant;

[0197] The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant and each adjacent quadrant is determined to obtain the wall trophoblast cell information; each designated sub-quadrant is the remaining sub-quadrants.

[0198] Optionally, the determination module 920 is specifically configured to determine the heterogeneity characteristic data of the trophoblast cells according to the number of trophoblast cells in each sub-quadrant;

[0199] According to the polar trophoblast cell information and the parietal trophoblast cell information, the mean distribution difference characteristic data and the mean difference characteristic data of the polar trophoblast cell and the parietal trophoblast cell are determined respectively;

[0200] According to the information of polar trophoblast cells and parietal trophoblast cells, the cell ratio between parietal trophoblast cells and polar trophoblast cells is determined;

[0201] The heterogeneity characteristic data of trophoblast cells, the mean distribution difference characteristic data, the mean difference characteristic data and the cell proportion are respectively used as a fusion characteristic data.

[0202] Optionally, the determination module 920 is specifically configured to determine the standard deviation of the number of trophoblast cells in each sub-quadrant according to the number of trophoblast cells in each sub-quadrant, so as to obtain heterogeneity characteristic data of the trophoblast cells.

[0203] Optionally, the determination module 920 is specifically configured to determine the median number of mural trophoblast cells based on the number of trophoblast cells in each designated sub-quadrant;

[0204] The standard deviation between the number of polar trophoblast cells and the median number of parietal trophoblast cells was determined to obtain mean distribution difference feature data;

[0205] The difference between the number of polar trophoblast cells and the median number of parietal trophoblast cells was determined to obtain mean difference feature data.

[0206] Optionally, the determination module 920 is specifically configured to determine the number of mural trophoblast cells according to the number of trophoblast cells in each designated sub-quadrant;

[0207] The ratio between the number of parietal trophoblast cells and the number of polar trophoblast cells was determined to obtain the cell fraction.

[0208] The above-mentioned device is used to execute the method provided by the aforementioned embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0209] The above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0210] The above modules can be connected or communicated with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may include a connection in the form of a LAN, a WAN, Bluetooth, a ZigBee, or NFC, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units. Those skilled in the art will clearly understand that for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application.

[0211] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, which may be a computing device with data processing capabilities.

[0212] The device may include: a processor 801 and a storage medium 802 .

[0213] The storage medium 802 is used to store programs, and the processor 801 calls the programs stored in the storage medium 802 to execute the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0214] Among them, the storage medium 802 stores program code, and when the program code is executed by the processor 801, the processor 801 executes various steps in the trophoblast cell characteristic analysis method according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0215] Processor 801 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor (DigitalSignal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware processor to be executed, or a combination of hardware and software modules in the processor can be executed.

[0216] Storage medium 802, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules. Storage medium may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type storage medium, random access storage medium (Random Access Memory, RAM), static random access storage medium (Static Random Access Memory, SRAM), programmable read-only storage medium (Programmable Read Only Memory, PROM), read-only storage medium (Read Only Memory, ROM), electrically erasable programmable read-only storage medium (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic storage medium, disk, optical disk, etc. Storage medium is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The storage medium 802 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0217] Optionally, the present application also provides a program product, such as a computer-readable storage medium, including a program, which is used to execute the above method embodiment when executed by a processor.

[0218] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0219] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0221] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only storage medium (English: Read-Only Memory, referred to as: ROM), random access storage medium (English: Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program code.

Claims

1. A method for analyzing trophoblast cell characteristics, characterized in that: include: Dividing the three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction into spatial quadrants to obtain a plurality of sub-quadrants; According to the inner cell mass information in each sub-quadrant, the polar trophoblast cell information and the parietal trophoblast cell information are determined; The polar trophoblast cell information includes: the number of polar trophoblast cells; the wall trophoblast cell information includes: the number of trophoblast cells in each designated sub-quadrant, the designated sub-quadrant is a quadrant that does not contain the polar trophoblast cells; Determining a plurality of fusion feature data according to the trophoblast cell information in each sub-quadrant, the polar trophoblast cell information, and the mural trophoblast cell information; According to the fusion feature data and basic feature data, the embryo development results are analyzed to obtain the estimated development result information of the target blastocyst.

2. The method according to claim 1, characterized in that The three-dimensional model of the target blastocyst obtained by three-dimensional reconstruction is divided into spatial quadrants to obtain a plurality of sub-quadrants, including: Taking the center of gravity of the target blastocyst three-dimensional model as the origin, the target blastocyst three-dimensional model is divided along the first preset direction, the second preset direction and the third preset direction to obtain the multiple sub-quadrants.

3. The method according to claim 1, characterized in that Determining the polar trophoblast cell information and the wall trophoblast cell information according to the inner cell mass information in each sub-quadrant includes: According to the information of the inner cell mass in each sub-quadrant, the target sub-quadrant where the center of gravity of the inner cell mass is located is determined; The polar trophoblast cell information and the parietal trophoblast cell information are determined according to the target sub-quadrant.

4. The method according to claim 3, characterized in that: The step of determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant includes: Determining the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant; The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

5. The method according to claim 3, characterized in that: The step of determining the polar trophoblast cell information and the wall trophoblast cell information according to the target sub-quadrant includes: Determining the polar trophoblast cell information according to the number of trophoblast cells in the target sub-quadrant and the number of trophoblast cells in each adjacent quadrant of the target sub-quadrant; The number of trophoblast cells in the remaining sub-quadrants except the target sub-quadrant and each adjacent quadrant is determined to obtain the wall trophoblast cell information; the designated sub-quadrants are the remaining sub-quadrants.

6. The method according to claim 1, characterized in that Determining a plurality of fusion feature data according to the trophoblast cell information in each sub-quadrant, the polar trophoblast cell information, and the wall trophoblast cell information includes: According to the number of trophoblast cells in each sub-quadrant, the heterogeneity characteristic data of trophoblast cells are determined; According to the polar trophoblast cell information and the parietal trophoblast cell information, respectively determining mean distribution difference characteristic data and mean difference characteristic data of the polar trophoblast cells and the parietal trophoblast cells; Determining the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information; The heterogeneity characteristic data of the trophoblast cells, the mean distribution difference characteristic data, the mean difference characteristic data and the cell proportion are respectively used as a fusion characteristic data.

7. The method according to claim 6, characterized in that Determining the heterogeneity characteristic data of trophoblast cells according to the number of trophoblast cells in each sub-quadrant includes: According to the number of trophoblast cells in each sub-quadrant, the standard deviation of the number of trophoblast cells in each sub-quadrant is determined to obtain the heterogeneity characteristic data of the trophoblast cells.

8. The method according to claim 6, characterized in that The step of determining the mean distribution difference characteristic data and mean difference characteristic data of the polar trophoblast cells and the parietal trophoblast cells respectively according to the polar trophoblast cell information and the parietal trophoblast cell information includes: Based on the number of trophoblast cells in each designated subquadrant, the median number of parietal trophoblast cells was determined; Determining the standard deviation between the number of the polar trophoblast cells and the median of the parietal trophoblast cells to obtain the mean distribution difference characteristic data; The difference between the number of the polar trophoblast cells and the median number of the parietal trophoblast cells is determined to obtain the mean difference feature data.

9. The method according to claim 6, characterized in that Determining the cell ratio between the parietal trophoblast cells and the polar trophoblast cells according to the polar trophoblast cell information and the parietal trophoblast cell information includes: Based on the number of trophoblast cells in each designated subquadrant, the number of mural trophoblast cells was determined; The ratio between the number of the parietal trophoblast cells and the number of the polar trophoblast cells is determined to obtain the cell ratio.

10. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to implement the trophoblast cell characteristic analysis method as described in any one of claims 1 to 9.

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