Method, device, electronic device and storage medium for determining stem cell density

Abstract

A method for determining stem cell density, the method comprising: obtaining a medical image to be detected; reducing the medical image according to a preset first reduction ratio to obtain a first reduced image; inputting the first reduced image into a first density detection model to obtain a first detection result; if the first detection result is that the stem cell density of the first reduced image is greater than or equal to the preset first density, determining that the stem cell density of the medical image is greater than or equal to the preset first density; if the first detection result is that the stem cell density of the first reduced image is less than the preset first density, detecting the medical image according to multiple preset second reduction ratios and multiple second density detection models to obtain at least one second detection result. The present invention also provides a device for determining stem cell density, an electronic device, and a storage medium. The present invention can improve the efficiency of obtaining stem cell density.

Description

Technical Field

[0001] The present invention relates to the field of image detection technology, and in particular to a method, device, electronic device and storage medium for determining stem cell density. Background Art

[0002] Currently, the density of stem cells in the entire image can be estimated by calculating the number and volume of stem cells in an image. However, in practice, it is found that calculating the number and volume of stem cells in an image takes a long time, resulting in low efficiency in obtaining the density of stem cells in the image.

[0003] Therefore, how to improve the efficiency of obtaining stem cell density is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above, it is necessary to provide a method, device, electronic device and storage medium for determining stem cell density, which can improve the efficiency of stem cell density detection.

[0005] A first aspect of the present invention provides a method for determining stem cell density, the method comprising:

[0006] Acquire a medical image to be detected;

[0007] Reducing the medical image according to a preset first reduction ratio to obtain a first reduced image;

[0008] Inputting the first reduced image into a first density detection model to obtain a first detection result;

[0009] If the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, determining that the stem cell density of the medical image is greater than or equal to the preset first density;

[0010] If the first detection result is that the stem cell density of the first reduced image is less than the preset first density, the medical image is detected according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models.

[0011] In a possible implementation, the method for determining stem cell density further includes:

[0012] If the at least one second detection result indicates that the stem cell density of the medical image is less than a preset second density, the medical image is input into a third density detection model to obtain the stem cell density of the medical image.

[0013] In a possible implementation, the detecting the medical image according to the preset multiple second reduction ratios and multiple second density detection models to obtain at least one second detection result includes:

[0014] Sorting the plurality of second reduction ratios in descending order to obtain a sorting result;

[0015] Reducing the medical image according to the largest second reduction ratio in the sorting results to obtain a second reduced image;

[0016] Inputting the second reduced image into a second density detection model corresponding to the second largest reduction ratio to obtain a second detection result corresponding to the second reduced image;

[0017] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is less than the second density corresponding to the largest second reduction ratio, determining a target reduction ratio adjacent to the largest second reduction ratio according to the sorting result;

[0018] Reducing the medical image according to the target reduction ratio to obtain a third reduced image;

[0019] The third reduced image is input into a second density detection model corresponding to the target reduction ratio to obtain a second detection result corresponding to the third reduced image.

[0020] In a possible implementation, the method for determining stem cell density further includes:

[0021] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density;

[0022] It is determined that the stem cell density of the medical image is greater than or equal to a second density corresponding to the maximum second reduction ratio and is less than the first density.

[0023] In a possible implementation, before acquiring the medical image to be detected, the method for determining stem cell density further includes:

[0024] Acquire a preset first training image;

[0025] From all the first training images, determining a first training image having a stem cell density greater than or equal to the first density as a first positive image, and determining a first training image having a stem cell density less than the first density as a first negative image;

[0026] According to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample;

[0027] The first positive sample and the first negative sample are used for training to obtain the trained first density detection model.

[0028] In a possible implementation, before acquiring the medical image to be detected, the method for determining stem cell density further includes:

[0029] Acquire a preset second training image;

[0030] For each of the second reduction ratios, from all the second training images, a second training image having a stem cell density greater than or equal to a second density corresponding to the second reduction ratio is determined as a second positive image, and a second training image having a stem cell density less than the second density corresponding to the second reduction ratio is determined as a second negative image;

[0031] According to the second reduction ratio, the second positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample;

[0032] The second positive sample and the second negative sample are used for training to obtain a trained second density detection model corresponding to the second reduction ratio.

[0033] As an optional implementation, before acquiring the medical image to be detected, the method for determining stem cell density further includes:

[0034] Acquire a preset third training image;

[0035] From all the third training images, determine the third training images whose stem cell density is greater than or equal to a preset third density as third positive samples, and determine the third training images whose stem cell density is less than the third density as third negative samples;

[0036] The third positive sample and the third negative sample are used for training to obtain the trained third density detection model.

[0037] A second aspect of the present invention provides a stem cell density determination device, the stem cell density determination device comprising:

[0038] An acquisition module, used for acquiring a medical image to be detected;

[0039] A reduction module, used for reducing the medical image according to a preset first reduction ratio to obtain a first reduced image;

[0040] An input module, used for inputting the first reduced image into a first density detection model to obtain a first detection result;

[0041] a determination module, configured to determine that the stem cell density of the medical image is greater than or equal to a preset first density if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density;

[0042] A detection module is used to detect the medical image according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result if the first detection result is that the stem cell density of the first reduced image is less than a preset first density, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models.

[0043] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the processor is configured to implement the method for determining stem cell density when executing a computer program stored in the memory.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the method for determining stem cell density when executed by a processor.

[0045] According to the above technical scheme, in the present invention, multiple reduction ratios and multiple density detection models can be used to perform hierarchical stem cell density detection on medical images. Starting from the medical image that is reduced by the preset maximum reduction ratio (the first reduction ratio) to the medical image that is not reduced, stem cell density detection is performed using the model with the largest stem cell density detected (the first density detection model) to the model with the smallest detection density (the third density detection model). For the reduced medical image, the stem cell density with a larger density is easy to detect, and the stem cell density with a smaller density is not easy to detect. Using different reduction ratios and corresponding detection models with different stem cell densities for detection, there is no need to calculate the number of stem cells to obtain the stem cell density, thereby improving the efficiency of stem cell density detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a preferred embodiment of a method for determining stem cell density disclosed in the present invention.

[0047] Figure 2 It is a functional module diagram of a preferred embodiment of a stem cell density determination device disclosed in the present invention.

[0048] Figure 3It is a schematic diagram of the structure of an electronic device of a preferred embodiment of the method for determining stem cell density of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0051] The method for determining stem cell density in the embodiment of the present invention is applied in an electronic device, and can also be applied in a hardware environment consisting of an electronic device and a server connected to the electronic device via a network, and is executed by the server and the electronic device together. The network includes but is not limited to: a wide area network, a metropolitan area network, or a local area network.

[0052] Among them, a server can refer to a computer system that can provide services to other devices (such as electronic devices) in the network. In a narrow sense, a server refers specifically to certain high-performance computers that can provide services to the outside world through the network. Compared with ordinary personal computers, it has higher requirements in terms of stability, security, performance, etc., so it is different from ordinary personal computers in terms of hardware such as CPU, chipset, memory, disk system, network, etc. However, if a personal computer can provide services such as but not limited to File Transfer Protocol (FTP) to the outside world, it can also be called a server.

[0053] The electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc. The electronic device may also include network devices and / or user devices. Among them, the network device includes but is not limited to a single network device, a server group composed of multiple network devices, or a cloud composed of a large number of hosts or network devices based on cloud computing (Cloud Computing), wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computer sets. The user device includes but is not limited to any electronic product that can interact with the user through a keyboard, mouse, remote control, touchpad or voice control device, such as a personal computer, tablet computer, smart phone, personal digital assistant (PDA), etc.

[0054] See also Figure 1 , Figure 1 The flowchart of a preferred embodiment of a method for determining stem cell density disclosed in the present invention is shown in FIG.

[0055] S11. Obtain a medical image to be detected.

[0056] The medical image may include but is not limited to stem cells, other cells and impurities.

[0057] As an optional implementation, before step S11, the method for determining stem cell density further includes:

[0058] Acquire a preset first training image;

[0059] From all the first training images, determining a first training image having a stem cell density greater than or equal to the first density as a first positive image, and determining a first training image having a stem cell density less than the first density as a first negative image;

[0060] According to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample;

[0061] The first positive sample and the first negative sample are used for training to obtain the trained first density detection model.

[0062] In this optional implementation, some training images may be prepared in advance, and these training images include medical images with different stem cell densities. The training images with stem cell density greater than or equal to the first density are determined as first positive images, and the training images with stem cell density less than the first density are determined as first negative images. For example, the first density may be 80%. Then, according to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample. For example, assuming that the first reduction ratio is 60%, the medical image is reduced by 60%. The first positive sample and the first negative sample are used for training to obtain the trained first density detection model. The first density detection model can perform density detection on the medical image reduced by 60% to determine whether the stem cell density in the medical image is greater than or equal to the first density.

[0063] As an optional implementation, before step S11, the method further includes:

[0064] Acquire a preset second training image;

[0065] For each of the second reduction ratios, from all the second training images, a second training image having a stem cell density greater than or equal to a second density corresponding to the second reduction ratio is determined as a second positive image, and a second training image having a stem cell density less than the second density corresponding to the second reduction ratio is determined as a second negative image;

[0066] According to the second reduction ratio, the second positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample;

[0067] The second positive sample and the second negative sample are used for training to obtain a trained second density detection model corresponding to the second reduction ratio.

[0068] In this optional implementation, some training images may be prepared in advance, and these training images include medical images with different stem cell densities. For each second density, a training image with a stem cell density greater than or equal to the second density is determined as a second image, and a training image with a stem cell density less than the second density is determined as a first negative image. For example, assuming that one of the second densities is 60%, the corresponding second reduction ratio is 40%. According to the corresponding second reduction ratio, the first positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample. The second positive sample and the second negative sample are used for training to obtain a trained second density detection model. The second density detection model can perform density detection on the medical image reduced by 40% to determine whether the stem cell density in the medical image is greater than or equal to the second density.

[0069] As an optional implementation, before step S11, the method further includes:

[0070] Acquire a preset third training image;

[0071] From all the third training images, determine the third training images whose stem cell density is greater than or equal to a preset third density as third positive samples, and determine the third training images whose stem cell density is less than the third density as third negative samples;

[0072] The third positive sample and the third negative sample are used for training to obtain the trained third density detection model.

[0073] In this optional implementation, some training images may be prepared in advance, including medical images with different stem cell densities, and training images with stem cell density greater than or equal to the third density are determined as third positive samples, and training images with stem cell density less than the first density are determined as third negative samples, for example, the third density may be 10%. The third positive samples and the third negative samples are used for training to obtain a trained third density detection model. The third density detection model may perform density detection on the medical image that has not been reduced, and determine whether the stem cell density in the medical image is greater than or equal to the third density.

[0074] S12, reducing the medical image according to a preset first reduction ratio to obtain a first reduced image;

[0075] In the embodiment of the present invention, the medical image may be reduced according to a preset first reduction ratio to obtain a first reduced image. Assuming that the first reduction ratio is 60%, the first reduced image is the medical image reduced by 60%.

[0076] S13: Input the first reduced image into a first density detection model to obtain a first detection result.

[0077] In an embodiment of the present invention, the first density detection model is used to determine whether the stem cell density of the first reduced image is greater than or equal to a preset first density, wherein the first density may be a larger stem cell density, such as 80%, and the first detection result may be that the stem cell density of the first reduced image is greater than or equal to 80%, or that the stem cell density of the first reduced image is less than 80%.

[0078] S14: If the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, determine that the stem cell density of the medical image is greater than or equal to the preset first density.

[0079] As an optional embodiment, the method for determining stem cell density further comprises:

[0080] If the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, determining that the stem cell density of the medical image is greater than or equal to the preset first density;

[0081] Outputting the stem cell density of the medical image.

[0082] In this optional implementation, if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, for example, if the first detection result is that the stem cell density of the first reduced image is greater than or equal to 80%, it is determined that the stem cell density of the medical image is greater than or equal to 80%. Text information "stem cell density is greater than or equal to 80%" may be output.

[0083] S15. If the first detection result is that the stem cell density of the first reduced image is less than the preset first density, the medical image is detected according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models.

[0084] The first reduction ratio is greater than any one of the plurality of second reduction ratios. For example, the first reduction ratio may be 60%, and the plurality of second reduction ratios may be 50%, 40%, 30%, 20%, etc. The detection densities of the plurality of second density detection models corresponding to the plurality of second reduction ratios may be 60%, 50%, 40%, 30%, etc.

[0085] Specifically, the detecting the medical image according to the preset multiple second reduction ratios and multiple second density detection models to obtain at least one second detection result includes:

[0086] Sorting the plurality of second reduction ratios in descending order to obtain a sorting result;

[0087] Reducing the medical image according to the largest second reduction ratio in the sorting results to obtain a second reduced image;

[0088] Inputting the second reduced image into a second density detection model corresponding to the second largest reduction ratio to obtain a second detection result corresponding to the second reduced image;

[0089] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is less than the second density corresponding to the largest second reduction ratio, determining a target reduction ratio adjacent to the largest second reduction ratio according to the sorting result;

[0090] Reducing the medical image according to the target reduction ratio to obtain a third reduced image;

[0091] The third reduced image is input into a second density detection model corresponding to the target reduction ratio to obtain a second detection result corresponding to the third reduced image.

[0092] In this optional embodiment, assuming that the multiple second reduction ratios are 50%, 40%, 30% and 20%, the detection densities of the multiple second density detection models corresponding to the multiple second reduction ratios can be 50%, 40%, 30% and 20%. First, the medical image can be reduced by 50% to obtain the second reduced image, and then the second reduced image can be input into the corresponding second density detection model with a detection density of 50% to obtain the second detection result corresponding to the second reduced image. The second detection result corresponding to the second reduced image can be that the stem cell density of the second reduced image is less than 50%, or that the stem cell density of the second reduced image is greater than or equal to 50%. If the target detection result can be that the stem cell density of the second reduced image is less than 50%, the medical image is reduced by 40% (the target reduction ratio adjacent to the largest second reduction ratio) to obtain the third reduced image. The third reduced image is input into a second density detection model corresponding to the target reduction ratio (i.e., the third reduced image is input into a second density detection model with a detection density of 40%), and a second detection result corresponding to the third reduced image may be that the stem cell density of the third reduced image is greater than or equal to 40%, or that the stem cell density of the third reduced image is less than 40%.

[0093] Optionally, if the second detection result corresponding to the third reduced image is that the stem cell density of the third reduced image is less than 40%, then according to the sorting result, the next reduction ratio (for example, 30%) is obtained, the medical image is reduced, and the corresponding second density detection model (for example, a second density detection model with a detection density of 30%) is used for detection.

[0094] As an optional embodiment, the method for determining stem cell density further comprises:

[0095] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density;

[0096] It is determined that the stem cell density of the medical image is greater than or equal to a second density corresponding to the maximum second reduction ratio and is less than the first density.

[0097] In this optional embodiment, if the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, there is no need to perform stem cell density detection on the image again to determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density; that is, it can be determined that the stem cell density of the medical image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density.

[0098] As an optional embodiment, the method for determining stem cell density further comprises:

[0099] If the at least one second detection result indicates that the stem cell density of the medical image is less than a preset second density, the medical image is input into a third density detection model to obtain the stem cell density of the medical image.

[0100] Among them, the first density is greater than any one of the multiple second densities, and the multiple second densities are all greater than the third density corresponding to the third density detection model.

[0101] The third density detection model is used to detect whether the stem cell density of the medical image is greater than or equal to a third density, and the third density is a relatively small stem cell density, such as 10%.

[0102] In an embodiment of the present invention, if the plurality of second detection results all indicate that the stem cell density of the medical image is less than a preset second density, then the medical image does not need to be reduced, and the medical image is directly input into the third density detection model. The result output by the third density detection model may be that the stem cell density of the medical image is less than 10%, or that the stem cell density of the medical image is greater than or equal to 10% and less than 20%.

[0103] exist Figure 1 In the described method flow, multiple reduction ratios and multiple density detection models can be used to perform hierarchical stem cell density detection on medical images. Starting from the medical image that is reduced by the preset maximum reduction ratio (the first reduction ratio) to the medical image that is not reduced, stem cell density detection is performed using the model with the largest stem cell density detected (the first density detection model) to the model with the smallest detection density (the third density detection model). For the reduced medical image, the stem cell density with a larger stem cell density is easy to detect, and the stem cell density with a smaller stem cell density is not easy to detect. Using different reduction ratios and corresponding detection models with different stem cell densities for detection, there is no need to calculate the number of stem cells to obtain the stem cell density, thereby improving the efficiency of stem cell density detection.

[0104] See also Figure 2 , Figure 2 It is a functional module diagram of a preferred embodiment of a stem cell density determination device disclosed in the present invention.

[0105] In some embodiments, the stem cell density determination device is run in an electronic device. The stem cell density determination device may include a plurality of functional modules composed of program code segments. The program code of each program segment in the stem cell density determination device may be stored in a memory and executed by at least one processor to perform Figure 1 Some or all of the steps in the described method for determining stem cell density.

[0106] In this embodiment, the stem cell density determination device can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 201, a reduction module 202, an input module 203, a detection module 204, a determination module 205, a training module 206 and an output module 207. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory.

[0107] The acquisition module 201 is used to acquire the medical image to be detected.

[0108] The medical image may include but is not limited to stem cells, other cells and impurities.

[0109] The reduction module 202 is used to reduce the medical image according to a preset first reduction ratio to obtain a first reduced image.

[0110] In the embodiment of the present invention, the medical image may be reduced according to a preset first reduction ratio to obtain a first reduced image. Assuming that the first reduction ratio is 60%, the first reduced image is the medical image reduced by 60%.

[0111] The input module 203 is used to input the first reduced image into a first density detection model to obtain a first detection result.

[0112] In an embodiment of the present invention, the first density detection model is used to determine whether the stem cell density of the first reduced image is greater than or equal to a preset first density, wherein the first density may be a larger stem cell density, such as 80%, and the first detection result may be that the stem cell density of the first reduced image is greater than or equal to 80%, or that the stem cell density of the first reduced image is less than 80%.

[0113] The determination module 205 is configured to determine that the stem cell density of the medical image is greater than or equal to a preset first density if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density.

[0114] As an optional embodiment, the stem cell density determination device may further include:

[0115] The determination module 205 is further configured to determine that the stem cell density of the medical image is greater than or equal to a preset first density if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density;

[0116] The output module 207 is used to output the stem cell density of the medical image.

[0117] In this optional implementation, if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, for example, if the first detection result is that the stem cell density of the first reduced image is greater than or equal to 80%, it is determined that the stem cell density of the medical image is greater than or equal to 80%. Text information "stem cell density is greater than or equal to 80%" may be output.

[0118] The detection module 204 is used to detect the medical image according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result if the first detection result is that the stem cell density of the first reduced image is less than a preset first density, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models.

[0119] The first reduction ratio is greater than one of the plurality of second reduction ratios. For example, the first reduction ratio may be 60%, and the plurality of second reduction ratios may be 50%, 40%, 30%, 20%, etc. The detection densities of the plurality of second density detection models corresponding to the plurality of second reduction ratios may be 60%, 50%, 40%, 30%, etc.

[0120] The input module 203 is further configured to input the medical image into a third density detection model to obtain the stem cell density of the medical image if at least one of the second detection results indicates that the stem cell density of the medical image is less than a preset second density.

[0121] Among them, the first density is greater than any one of the multiple second densities, and the multiple second densities are all greater than the third density corresponding to the third density detection model.

[0122] The third density detection model is used to detect whether the stem cell density of the medical image is greater than or equal to a third density, and the third density is a relatively small stem cell density, such as 10%.

[0123] In an embodiment of the present invention, if the second detection result is that the stem cell density of the medical image is less than a plurality of preset second densities, then the medical image does not need to be reduced, and the medical image is directly input into the third density detection model. The result output by the third density detection model may be that the stem cell density of the medical image is less than 10%, or that the stem cell density of the medical image is greater than or equal to 10% and less than 20%.

[0124] As an optional implementation, the detection module 204 detects the medical image according to the preset multiple second reduction ratios and multiple second density detection models, and obtains at least one second detection result in the following manner:

[0125] Sorting the plurality of second reduction ratios in descending order to obtain a sorting result;

[0126] Reducing the medical image according to the largest second reduction ratio in the sorting results to obtain a second reduced image;

[0127] Inputting the second reduced image into a second density detection model corresponding to the second largest reduction ratio to obtain a second detection result corresponding to the second reduced image;

[0128] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is less than the second density corresponding to the largest second reduction ratio, determining a target reduction ratio adjacent to the largest second reduction ratio according to the sorting result;

[0129] Reducing the medical image according to the target reduction ratio to obtain a third reduced image;

[0130] The third reduced image is input into a second density detection model corresponding to the target reduction ratio to obtain a second detection result corresponding to the third reduced image.

[0131] In this optional embodiment, assuming that the multiple second reduction ratios are 50%, 40%, 30% and 20%, the detection densities of the multiple second density detection models corresponding to the multiple second reduction ratios can be 50%, 40%, 30% and 20%. First, the medical image can be reduced by 50% to obtain the second reduced image, and then the second reduced image can be input into the corresponding second density detection model with a detection density of 50% to obtain the second detection result corresponding to the second reduced image. The second detection result corresponding to the second reduced image can be that the stem cell density of the second reduced image is less than 50%, or that the stem cell density of the second reduced image is greater than or equal to 50%. If the target detection result can be that the stem cell density of the second reduced image is less than 50%, the medical image is reduced by 40% (the target reduction ratio adjacent to the largest second reduction ratio) to obtain the third reduced image. The third reduced image is input into a second density detection model corresponding to the target reduction ratio (i.e., the third reduced image is input into a second density detection model with a detection density of 40%), and a second detection result corresponding to the third reduced image may be that the stem cell density of the third reduced image is greater than or equal to 40%, or that the stem cell density of the third reduced image is less than 40%.

[0132] Optionally, if the second detection result corresponding to the third reduced image is that the stem cell density of the third reduced image is less than 40%, then according to the sorting result, the next reduction ratio (for example, 30%) is obtained, the medical image is reduced, and the corresponding second density detection model (for example, a second density detection model with a detection density of 30%) is used for detection.

[0133] As an optional embodiment, the stem cell density determination device may further include:

[0134] The determination module 205 is further configured to determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density if the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio;

[0135] The determination module 205 is further configured to determine that the stem cell density of the medical image is greater than or equal to a second density corresponding to the maximum second reduction ratio and is less than the first density.

[0136] In this optional embodiment, if the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, there is no need to perform stem cell density detection on the image again to determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density; that is, it can be determined that the stem cell density of the medical image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density.

[0137] As an optional implementation, the acquisition module 201 is further used to acquire a preset first training image;

[0138] The determination module 205 is further configured to determine, from among all the first training images, a first training image having a stem cell density greater than or equal to the first density as a first positive image, and a first training image having a stem cell density less than the first density as a first negative image;

[0139] The reduction module 202 is further configured to reduce the first positive image according to the first reduction ratio to obtain a first positive sample, and to reduce the first negative image according to the first reduction ratio to obtain a first negative sample;

[0140] The training module 206 is used to perform training using the first positive sample and the first negative sample to obtain the trained first density detection model.

[0141] In this optional implementation, some training images may be prepared in advance, and these training images include medical images with different stem cell densities. The training images with stem cell density greater than or equal to the first density are determined as first positive images, and the training images with stem cell density less than the first density are determined as first negative images. For example, the first density may be 80%. Then, according to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample. For example, assuming that the first reduction ratio is 60%, the medical image is reduced by 60%. The first positive sample and the first negative sample are used for training to obtain the trained first density detection model. The first density detection model can perform density detection on the medical image reduced by 60% to determine whether the stem cell density in the medical image is greater than or equal to the first density.

[0142] As an optional implementation, the acquisition module 201 is further used to acquire a preset second training image;

[0143] The determination module 205 is further configured to, for each second reduction ratio, determine, from all the second training images, a second training image having a stem cell density greater than or equal to a second density corresponding to the second reduction ratio as a second positive image, and determine a second training image having a stem cell density less than the second density corresponding to the second reduction ratio as a second negative image;

[0144] The reduction module 202 is further configured to reduce the second positive image according to the second reduction ratio to obtain a second positive sample, and to reduce the second negative image according to the second reduction ratio to obtain a second negative sample;

[0145] The training module 206 is further configured to perform training using the second positive sample and the second negative sample to obtain a trained second density detection model corresponding to the second reduction ratio.

[0146] In this optional implementation, some training images may be prepared in advance, and these training images include medical images with different stem cell densities. For each second density, a training image with a stem cell density greater than or equal to the second density is determined as a second image, and a training image with a stem cell density less than the second density is determined as a first negative image. For example, assuming that one of the second densities is 60%, the corresponding second reduction ratio is 40%. According to the corresponding second reduction ratio, the first positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample. The second positive sample and the second negative sample are used for training to obtain a trained second density detection model. The second density detection model can perform density detection on the medical image reduced by 40% to determine whether the stem cell density in the medical image is greater than or equal to the second density.

[0147] As an optional implementation, the acquisition module 201 is further used to acquire a preset third training image;

[0148] The determination module 205 is further configured to determine, from among all the third training images, a third training image having a stem cell density greater than or equal to a preset third density as a third positive sample, and a third training image having a stem cell density less than the third density as a third negative sample;

[0149] The training module 206 is further configured to perform training using the third positive sample and the third negative sample to obtain the trained third density detection model.

[0150] In this optional implementation, some training images may be prepared in advance, including medical images with different stem cell densities, and training images with stem cell density greater than or equal to the third density are determined as third positive samples, and training images with stem cell density less than the first density are determined as third negative samples, for example, the third density may be 10%. The third positive samples and the third negative samples are used for training to obtain a trained third density detection model. The third density detection model may perform density detection on the medical image that has not been reduced, and determine whether the stem cell density in the medical image is greater than or equal to the third density.

[0151] exist Figure 2 In the described stem cell density determination device, multiple reduction ratios and multiple density detection models can be used to perform hierarchical stem cell density detection on medical images. Starting from the medical image that is reduced by the preset maximum reduction ratio (the first reduction ratio) to the medical image that is not reduced, stem cell density detection is performed using the model with the largest detected stem cell density (the first density detection model) to the model with the smallest detected density (the third density detection model). For the reduced medical image, the stem cell density with a larger density is easy to detect, while the stem cell density with a smaller density is not easy to detect. Using different reduction ratios and corresponding detection models with different stem cell densities for detection, there is no need to calculate the number of stem cells to obtain the stem cell density, thereby improving the efficiency of stem cell density detection.

[0152] like Figure 3 As shown, Figure 3 Schematic diagram of the structure of an electronic device for implementing a preferred embodiment of the method for determining stem cell density of the present invention. The electronic device 3 includes a memory 31, at least one processor 32, a computer program 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.

[0153] Those skilled in the art will understand that Figure 3 The schematic diagram shown is merely an example of the electronic device 3 and does not constitute a limitation on the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 3 may also include input and output devices, network access devices, etc.

[0154] The electronic device 3 also includes but is not limited to any electronic product that can interact with the user through a keyboard, mouse, remote control, touch pad or voice control device, such as a personal computer, tablet computer, smart phone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc. The network where the electronic device 3 is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0155] The at least one processor 32 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, discrete hardware components, etc. The processor 32 may be a microprocessor or any conventional processor, etc. The processor 32 is the control center of the electronic device 3, and uses various interfaces and lines to connect various parts of the entire electronic device 3.

[0156] The memory 31 can be used to store the computer program 33 and / or modules / units. The processor 32 implements various functions of the electronic device 3 by running or executing the computer program and / or modules / units stored in the memory 31 and calling the data stored in the memory 31. The memory 31 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the electronic device 3, etc. In addition, the memory 31 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, etc.

[0157] Combination Figure 1The memory 31 in the electronic device 3 stores a plurality of instructions to implement a method for determining stem cell density, and the processor 32 can execute the plurality of instructions to implement:

[0158] Acquire a medical image to be detected;

[0159] Reducing the medical image according to a preset first reduction ratio to obtain a first reduced image;

[0160] Inputting the first reduced image into a first density detection model to obtain a first detection result;

[0161] If the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, determining that the stem cell density of the medical image is greater than or equal to the preset first density;

[0162] If the first detection result is that the stem cell density of the first reduced image is less than the preset first density, the medical image is detected according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models.

[0163] As an optional implementation, the processor 32 may execute the multiple instructions to achieve:

[0164] If the at least one second detection result indicates that the stem cell density of the medical image is less than a preset second density, the medical image is input into a third density detection model to obtain the stem cell density of the medical image.

[0165] As an optional implementation, the detecting the medical image according to the preset multiple second reduction ratios and multiple second density detection models to obtain at least one second detection result includes:

[0166] Sorting the plurality of second reduction ratios in descending order to obtain a sorting result;

[0167] Reducing the medical image according to the largest second reduction ratio in the sorting results to obtain a second reduced image;

[0168] Inputting the second reduced image into a second density detection model corresponding to the second largest reduction ratio to obtain a second detection result corresponding to the second reduced image;

[0169] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is less than the second density corresponding to the largest second reduction ratio, determining a target reduction ratio adjacent to the largest second reduction ratio according to the sorting result;

[0170] Reducing the medical image according to the target reduction ratio to obtain a third reduced image;

[0171] The third reduced image is input into a second density detection model corresponding to the target reduction ratio to obtain a second detection result corresponding to the third reduced image.

[0172] As an optional implementation, the processor 32 may execute the multiple instructions to achieve:

[0173] If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density;

[0174] It is determined that the stem cell density of the medical image is greater than or equal to a second density corresponding to the maximum second reduction ratio and is less than the first density.

[0175] As an optional implementation, before acquiring the medical image to be detected, the processor 32 may execute the multiple instructions to achieve:

[0176] Acquire a preset first training image;

[0177] From all the first training images, determining a first training image having a stem cell density greater than or equal to the first density as a first positive image, and determining a first training image having a stem cell density less than the first density as a first negative image;

[0178] According to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample;

[0179] The first positive sample and the first negative sample are used for training to obtain the trained first density detection model.

[0180] As an optional implementation, before acquiring the medical image to be detected, the processor 32 may execute the multiple instructions to achieve:

[0181] Acquire a preset second training image;

[0182] For each of the second reduction ratios, from all the second training images, a second training image having a stem cell density greater than or equal to a second density corresponding to the second reduction ratio is determined as a second positive image, and a second training image having a stem cell density less than the second density corresponding to the second reduction ratio is determined as a second negative image;

[0183] According to the second reduction ratio, the second positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample;

[0184] The second positive sample and the second negative sample are used for training to obtain a trained second density detection model corresponding to the second reduction ratio.

[0185] As an optional implementation, before acquiring the medical image to be detected, the processor 32 may execute the multiple instructions to achieve:

[0186] Acquire a preset third training image;

[0187] From all the third training images, determine the third training images whose stem cell density is greater than or equal to a preset third density as third positive samples, and determine the third training images whose stem cell density is less than the third density as third negative samples;

[0188] The third positive sample and the third negative sample are used for training to obtain the trained third density detection model.

[0189] Specifically, the specific implementation method of the processor 32 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0190] exist Figure 3 In the described electronic device 3, multiple reduction ratios and multiple density detection models can be used to perform hierarchical stem cell density detection on medical images. Starting from the medical image that is reduced by the preset maximum reduction ratio (the first reduction ratio) to the medical image that is not reduced, stem cell density detection is performed using the model with the largest stem cell density (the first density detection model) to the model with the smallest detection density (the third density detection model). For the reduced medical image, the stem cell density with a larger stem cell density is easy to detect, and the stem cell density with a smaller stem cell density is not easy to detect. Using different reduction ratios and corresponding detection models with different stem cell densities for detection, there is no need to calculate the number of stem cells to obtain the stem cell density, thereby improving the efficiency of stem cell density detection.

[0191] If the module / unit integrated in the electronic device 3 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program code can be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

[0192] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0193] The modules described as separate components may or may not be physically separated, and the components shown as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, 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 modules.

[0195] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any accompanying figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim may also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, and do not indicate any particular order.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention. For example, the order of the steps in the flowchart may be changed and some steps may be omitted according to different requirements.

Claims

1. A method for determining stem cell density, characterized in that: The method for determining stem cell density comprises: Acquire a medical image to be detected; Reducing the medical image according to a preset first reduction ratio to obtain a first reduced image; Inputting the first reduced image into a first density detection model to obtain a first detection result; If the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density, determining that the stem cell density of the medical image is greater than or equal to the preset first density; If the first detection result is that the stem cell density of the first reduced image is less than a preset first density, the medical image is detected according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result, wherein the plurality of second reduction ratios correspond one-to-one to the plurality of second density detection models; The multiple second reduction ratios are sorted in descending order to obtain a sorting result; the medical image is reduced according to the largest second reduction ratio in the sorting result to obtain a second reduced image; the second reduced image is input into a second density detection model corresponding to the largest second reduction ratio to obtain a second detection result corresponding to the second reduced image.

2. The method for determining stem cell density according to claim 1, characterized in that: The method for determining stem cell density further comprises: If the at least one second detection result indicates that the stem cell density of the medical image is less than a preset second density, the medical image is input into a third density detection model to obtain the stem cell density of the medical image.

3. The method for determining stem cell density according to claim 1, characterized in that: The detecting the medical image according to the preset multiple second reduction ratios and multiple second density detection models to obtain at least one second detection result includes: If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is less than the second density corresponding to the largest second reduction ratio, determining a target reduction ratio adjacent to the largest second reduction ratio according to the sorting result; Reducing the medical image according to the target reduction ratio to obtain a third reduced image; The third reduced image is input into a second density detection model corresponding to the target reduction ratio to obtain a second detection result corresponding to the third reduced image.

4. The method for determining stem cell density according to claim 3, characterized in that: The method for determining stem cell density further comprises: If the second detection result corresponding to the second reduced image is that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio, determine that the stem cell density of the second reduced image is greater than or equal to the second density corresponding to the maximum second reduction ratio and less than the first density; It is determined that the stem cell density of the medical image is greater than or equal to a second density corresponding to the maximum second reduction ratio and is less than the first density.

5. The method for determining stem cell density according to any one of claims 1 to 4, characterized in that: Before acquiring the medical image to be detected, the method for determining stem cell density further includes: Acquire a preset first training image; From all the first training images, determining a first training image having a stem cell density greater than or equal to the first density as a first positive image, and determining a first training image having a stem cell density less than the first density as a first negative image; According to the first reduction ratio, the first positive image is reduced to obtain a first positive sample, and according to the first reduction ratio, the first negative image is reduced to obtain a first negative sample; The first positive sample and the first negative sample are used for training to obtain the trained first density detection model.

6. The method for determining stem cell density according to any one of claims 1 to 4, characterized in that: Before acquiring the medical image to be detected, the method for determining stem cell density further includes: Acquire a preset second training image; For each of the second reduction ratios, from all the second training images, a second training image having a stem cell density greater than or equal to a second density corresponding to the second reduction ratio is determined as a second positive image, and a second training image having a stem cell density less than the second density corresponding to the second reduction ratio is determined as a second negative image; According to the second reduction ratio, the second positive image is reduced to obtain a second positive sample, and according to the second reduction ratio, the second negative image is reduced to obtain a second negative sample; The second positive sample and the second negative sample are used for training to obtain a trained second density detection model corresponding to the second reduction ratio.

7. The method for determining stem cell density according to any one of claims 2 to 4, characterized in that: Before acquiring the medical image to be detected, the method for determining stem cell density further includes: Acquire a preset third training image; From all the third training images, determine the third training images whose stem cell density is greater than or equal to a preset third density as third positive samples, and determine the third training images whose stem cell density is less than the third density as third negative samples; The third positive sample and the third negative sample are used for training to obtain the trained third density detection model.

8. A device for determining stem cell density, characterized in that: The stem cell density determination device comprises: An acquisition module, used for acquiring a medical image to be detected; A reduction module, used for reducing the medical image according to a preset first reduction ratio to obtain a first reduced image; An input module, used for inputting the first reduced image into a first density detection model to obtain a first detection result; a determination module, configured to determine that the stem cell density of the medical image is greater than or equal to a preset first density if the first detection result is that the stem cell density of the first reduced image is greater than or equal to a preset first density; a detection module, configured to detect the medical image according to a plurality of preset second reduction ratios and a plurality of second density detection models to obtain at least one second detection result if the first detection result is that the stem cell density of the first reduced image is less than a preset first density, wherein the plurality of second reduction ratios correspond to the plurality of second density detection models in a one-to-one manner; The detection module is also used to sort the multiple second reduction ratios in descending order to obtain a sorting result; reduce the medical image according to the largest second reduction ratio in the sorting result to obtain a second reduced image; input the second reduced image into a second density detection model corresponding to the largest second reduction ratio to obtain a second detection result corresponding to the second reduced image.

9. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for determining stem cell density according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for determining stem cell density according to any one of claims 1 to 7 is implemented.

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