Evaluation method of adherent cell digestion results, cell culture method and terminal equipment

Abstract

The present invention discloses an evaluation method for adherent cell digestion results, a cell culture method and a terminal device. The evaluation method for adherent cell digestion results comprises: obtaining an adherent cell image after digestion; performing image processing on the adherent cell image after digestion to obtain the adherent cell area after digestion; calculating the digestion yield according to the adherent cell area after digestion and the bottom area of ​​the cell culture container; or calculating the digestion yield according to the adherent cell area after digestion and the adherent cell area before digestion; evaluating the adherent cell digestion results according to the digestion yield to obtain a digestion evaluation result. The present invention can avoid the error caused by manually evaluating the digestion results of adherent cells, so as to improve the accuracy of the digestion evaluation results of adherent cells, thereby ensuring the cell viability of adherent cells during the culture process.

Description

Technical Field

[0001] The invention relates to the field of biotechnology, and in particular to an evaluation method for adherent cell digestion results, a cell culture method and terminal equipment. Background Art

[0002] During the subculture of adherent cells, when the cells reach a suitable growth confluence, they need to be dissociated from the surface of the cell culture container by enzymatic digestion, resuspended into a single cell suspension after centrifugation with fresh culture medium, and placed in a new cell culture container for continued culture. This process is cell subculture digestion. During the culture of adherent cells, the quality of the adherent cell digestion process will directly affect the cell viability of the cultured adherent cells.

[0003] At present, the traditional method of evaluating the digestion results of adherent cells is mainly to perform manual observation by inverted microscope instruments, and roughly estimate the proportion of cells in the field of view based on the area of ​​cells remaining at the bottom of the culture container after digestion to evaluate the digestion results of adherent cells. However, this evaluation method is heavily dependent on subjective factors such as the experience and level of the operator, which will inevitably lead to errors in the evaluation results due to human factors, resulting in a low accuracy rate of the evaluation results of the digestion results of adherent cells. Summary of the invention

[0004] The present invention provides an adherent cell digestion result evaluation method, a cell culture method and a terminal device, which can avoid the error caused by manually evaluating the adherent cell digestion result, so as to improve the accuracy of the adherent cell digestion evaluation result, thereby ensuring the cell viability of the adherent cells during the culture process.

[0005] The embodiment of the present invention provides a method for evaluating adherent cell digestion results, comprising:

[0006] Acquire images of adherent cells after digestion;

[0007] performing image processing on the image of the adherent cells after digestion to obtain the area of ​​the adherent cells after digestion;

[0008] According to the first expression 1-(S 消化后的贴壁细胞 ÷S 细胞培养容器 ), and the digestibility was calculated; where S 消化后的贴壁细胞 is the adherent cell area after digestion, S 细胞培养容器 is the bottom area of ​​the cell culture container obtained in advance;

[0009] Or, according to the second expression 1-(S 消化后的贴壁细胞 ÷S 消化前的贴壁细胞 ), and the digestibility was calculated; where S 消化前的贴壁细胞is the area of ​​attached cells obtained before digestion;

[0010] The adherent cell digestion result is evaluated according to the digestion yield to obtain a digestion evaluation result.

[0011] As an improvement of the above scheme, the image processing of the digested adherent cell image to obtain the digested adherent cell area includes:

[0012] Inputting the digested adherent cell image into a pre-trained deep learning model for image processing to obtain a digested adherent cell mask image; wherein the deep learning model is trained based on the training adherent cell image and the corresponding adherent cell mask image;

[0013] Based on the adherent cell mask image after digestion, the adherent cell area after digestion was calculated.

[0014] As an improvement of the above scheme, the calculation of the area of ​​adherent cells after digestion based on the mask image of adherent cells after digestion includes:

[0015] Performing image grayscale processing on the digested adherent cell mask image to obtain a digested adherent cell grayscale image;

[0016] The area of ​​the adherent cells after digestion is obtained by calculating the area of ​​the region in which the pixel grayscale value in the grayscale image of the adherent cells after digestion is greater than a preset grayscale threshold.

[0017] As an improvement of the above solution, the step of calculating the area of ​​the region in which the grayscale value of pixels in the grayscale image of the digested adherent cells is greater than a first preset grayscale threshold to obtain the area of ​​the adherent cells after digestion includes:

[0018] A first preset grayscale threshold value is set for the non-0 value area and the 0 value area in the grayscale image of the adherent cells after digestion, the area where the adherent cells are located is divided into a non-0 value area, and the area outside the adherent cells is divided into a 0 value area, and the area of ​​the non-0 value area in the grayscale image of the adherent cells after digestion is calculated to obtain the area of ​​the adherent cells after digestion.

[0019] As an improvement of the above solution, the deep learning model is trained in the following way:

[0020] Acquire multiple images of adherent cells for training;

[0021] Annotating the adherent cells in each of the adherent cell images for training to obtain a label file for each of the adherent cell images for training;

[0022] Convert each of the label files into a corresponding adherent cell mask image;

[0023] The pre-constructed deep learning model is trained using a plurality of the training adherent cell images and an adherent cell mask image corresponding to each of the training adherent cell images to obtain a trained deep learning model.

[0024] As an improvement of the above scheme, the digestion result of adherent cells is evaluated according to the digestion yield to obtain the digestion evaluation result, including:

[0025] When the digestion yield is greater than a preset digestion threshold, the digestion evaluation result is determined to meet the passage condition; wherein the numerical range of the preset digestion threshold is: 95%-100%;

[0026] When the digestion yield is less than or equal to the preset digestion threshold, the digestion evaluation result is determined to be not meeting the subculture condition.

[0027] Accordingly, another embodiment of the present invention provides a cell culture method, comprising:

[0028] During the culture of adherent cells, obtain images of adherent cells before digestion;

[0029] performing image processing on the image of adherent cells before digestion to obtain the area of ​​adherent cells before digestion;

[0030] Obtain the bottom area of ​​the cell culture container where the adherent cells are located;

[0031] Calculating the ratio of the adherent cell area before digestion to the bottom area to obtain the degree of confluence;

[0032] When the degree of confluence is within a preset degree of confluence range, performing cell digestion on the adherent cells in the cell culture container by an enzyme digestion method;

[0033] In the process of performing cell digestion on the adherent cells in the cell culture container, based on any one of the methods for evaluating adherent cell digestion results described above, the adherent cell digestion result is evaluated to obtain a digestion evaluation result;

[0034] When the digestion evaluation result is that the subculture condition is met, adding a digestion stop solution to the cell culture container to stop the cell digestion, and repeatedly washing the cell culture container after stopping the cell digestion to obtain a cell suspension;

[0035] The cell suspension is centrifuged, and the cells precipitated by centrifugation are placed in a new cell culture container for cell culture.

[0036] As an improvement of the above scheme, the image processing of the adherent cell image before digestion to obtain the adherent cell area before digestion specifically includes:

[0037] Inputting the image of adherent cells before digestion into a pre-trained deep learning model for image processing to obtain a mask image of adherent cells before digestion; wherein the deep learning model is trained based on the training adherent cell image and the corresponding mask image of adherent cells;

[0038] Based on the mask image of adherent cells before digestion, the area of ​​adherent cells before digestion was calculated.

[0039] As an improvement of the above scheme, the mask image of the adherent cells before digestion is subjected to image grayscale processing to obtain a grayscale image of the adherent cells before digestion;

[0040] A second preset grayscale threshold is set for the non-0 value area and the 0 value area in the grayscale image of the adherent cells before digestion, the area where the adherent cells are located is divided into a non-0 value area, and the area outside the adherent cells is divided into a 0 value area, the area of ​​the non-0 value area in the grayscale image of the adherent cells before digestion is calculated, and the area of ​​the area in the grayscale image of the adherent cells before digestion where the pixel grayscale value is greater than the second preset grayscale threshold is calculated to obtain the area of ​​the adherent cells before digestion.

[0041] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the adherent cell digestion result as described in any one of the above items and / or the cell culture method as described in any one of the above items are implemented.

[0042] Compared with the prior art, the evaluation method of adherent cell digestion results, cell culture method and terminal device disclosed in the embodiment of the present invention obtain an image of adherent cells after digestion; perform image processing on the image of adherent cells after digestion to obtain the area of ​​adherent cells after digestion; calculate the digestion yield according to the area of ​​adherent cells after digestion and the bottom area of ​​the cell culture container obtained in advance; or calculate the digestion yield according to the area of ​​adherent cells after digestion and the area of ​​adherent cells before digestion obtained in advance; wherein the digestion yield is the ratio of the number of cells in a suspended state obtained by digestion in the cell culture container to the total number of cells; the digestion result of adherent cells is evaluated according to the digestion yield to obtain a digestion evaluation result. Therefore, the error caused by manually evaluating the digestion result of adherent cells can be avoided to improve the accuracy of the digestion evaluation result of adherent cells, thereby ensuring the cell viability of adherent cells during the culture process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flow chart of a method for evaluating adherent cell digestion results provided by an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of an image of adherent cells after digestion provided by an embodiment of the present invention;

[0045] Figure 3 is a schematic flow chart of a cell culture method provided by an embodiment of the present invention;

[0046] Figure 4 is a schematic diagram of an image of adherent cells before digestion provided by an embodiment of the present invention;

[0047] Figure 5 It is a structural diagram of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] See also Figure 1 , Figure 1 It is a schematic flow chart of a method for evaluating adherent cell digestion results provided by one embodiment of the present invention.

[0050] The method for evaluating adherent cell digestion results provided in an embodiment of the present invention comprises the steps of:

[0051] S11, acquiring images of adherent cells after digestion;

[0052] S12, performing image processing on the image of the adherent cells after digestion to obtain the area of ​​the adherent cells after digestion;

[0053] S13, according to the first expression 1-(S 消化后的贴壁细胞 -S 细胞培养容器 ), and the digestibility was calculated; where S 消化后的贴壁细胞 is the adherent cell area after digestion, S 细胞培养容器 is the bottom area of ​​the cell culture container obtained in advance; or, according to the second expression 1-(S 消化后的贴壁细胞 -S 消化前的贴壁细胞 ), and the digestibility was calculated; where S 消化前的贴壁细胞 is the area of ​​attached cells obtained before digestion;

[0054] S14. Evaluate the adherent cell digestion result according to the digestion yield to obtain a digestion evaluation result.

[0055] Specifically, in actual operation, the image of adherent cells after digestion is obtained in the following manner: the cell culture container containing the adherent cells to be passaged is placed in a biosafety cabinet, and after the culture solution is poured out, an appropriate amount of PBS (phosphate buffered saline) is added to wash away the residual culture medium in the cell culture container. After discarding the washing solution, an appropriate volume of trypsin (Trypsin-EDTA) is added to digest the cells until the cells are observed to be translucent and round under a microscope and float up when gently shaken. At this time, the cell culture container is placed under a microscope again for observation, the field of view is adjusted, a 10X objective lens is selected, and the cells in the cell culture container are photographed and saved using the instrument imaging software to obtain the following image: Figure 2 The image of adherent cells after digestion is shown. Subsequently, the digestion yield is calculated by the evaluation method of the adherent cell digestion result of this embodiment, and the adherent cell digestion result is evaluated according to the digestion yield to obtain the digestion evaluation result. When the digestion evaluation result meets the passage condition, an equal volume of digestion stop solution is added to stop the digestion, and the remaining cells are repeatedly rinsed with a pipette, the cell suspension is collected and centrifuged, and the cells precipitated by centrifugation are placed in a suitable culture system for continued culture.

[0056] As one of the preferred embodiments, the image processing of the digested adherent cell image to obtain the digested adherent cell area includes:

[0057] Inputting the digested adherent cell image into a pre-trained deep learning model for image processing to obtain a digested adherent cell mask image; wherein the deep learning model is trained based on the training adherent cell image and the corresponding adherent cell mask image;

[0058] Based on the adherent cell mask image after digestion, the adherent cell area after digestion was calculated.

[0059] Further, the calculating the area of ​​adherent cells after digestion based on the adherent cell mask image after digestion comprises:

[0060] Performing image grayscale processing on the digested adherent cell mask image to obtain a digested adherent cell grayscale image;

[0061] The area of ​​the adherent cells after digestion is obtained by calculating the area of ​​the region in which the pixel grayscale value in the grayscale image of the adherent cells after digestion is greater than a preset grayscale threshold.

[0062] It should be noted that the mask image is a binary image composed of 0 and 1. When the mask is applied in a certain function, the 1-value area in the mask image is processed, and the shielded 0-value area is not included in the calculation. Therefore, in an embodiment, the non-cell area on the image can be shielded by the digested adherent cell mask image, and the area of ​​the adherent cells can be extracted. Specifically, the image grayscale processing method is used to perform image grayscale processing on the digested adherent cell mask image, extract the single-channel adherent cell image, obtain the digested adherent cell grayscale image, set the preset grayscale threshold of the non-0 value area and the 0 value area, divide the area where the adherent cells are located into the non-0 value area, and divide the area outside the adherent cells into the 0 value area, calculate the area of ​​the non-0 value area in the digested adherent cell grayscale image, and obtain the digested adherent cell area.

[0063] As a specific embodiment, the deep learning model is trained in the following way:

[0064] Acquire multiple images of adherent cells for training;

[0065] Annotating the adherent cells in each of the adherent cell images for training to obtain a label file for each of the adherent cell images for training;

[0066] Convert each of the label files into a corresponding adherent cell mask image;

[0067] The pre-constructed deep learning model is trained using a plurality of the training adherent cell images and an adherent cell mask image corresponding to each of the training adherent cell images to obtain a trained deep learning model.

[0068] It should be noted that in actual operation, the adherent cells in each of the training adherent cell images can be annotated by annotation software to obtain a label file for each of the training adherent cell images, so that the pre-constructed deep learning model is trained by multiple training adherent cell images and the adherent cell mask map corresponding to each of the training adherent cell images to adjust the parameters of the deep learning model and obtain a trained deep learning model. When the digested adherent cell image is input into the pre-trained deep learning model, the trained deep learning model can be used for inference to obtain the digested adherent cell mask map.

[0069] Specifically, the calculating the digestion yield according to the area of ​​the adherent cells after the digestion and the pre-acquired bottom area of ​​the cell culture container specifically includes:

[0070] Calculating a first ratio of the area of ​​the adherent cells after digestion to the bottom area of ​​the cell culture container;

[0071] Calculate the first expression 1-(S 消化后的贴壁细胞 ÷S 细胞培养容器 ), where S 消化后的贴壁细胞 is the adherent cell area after digestion, S 细胞培养容器 is the bottom area of ​​the cell culture container obtained in advance; and the digestion yield is obtained.

[0072] Optionally, the bottom area of ​​the cell culture container is obtained by the following steps:

[0073] The image of the adherent cells after digestion is processed based on an image processing method to obtain the bottom area of ​​the cell culture container.

[0074] Optionally, the area of ​​adherent cells before digestion is obtained by the following steps:

[0075] Acquire images of adherent cells before digestion;

[0076] The image of the adherent cells before digestion is processed to obtain the area of ​​the adherent cells before digestion.

[0077] As one of the preferred implementations, the image processing of the adherent cell image before digestion to obtain the adherent cell area before digestion specifically includes:

[0078] Inputting the image of adherent cells before digestion into a pre-trained deep learning model for image processing to obtain a mask image of adherent cells before digestion;

[0079] Based on the mask image of adherent cells before digestion, the area of ​​adherent cells before digestion was calculated.

[0080] In a specific embodiment, the digestion result of adherent cells is evaluated according to the digestion yield to obtain the digestion evaluation result, including:

[0081] When the digestion yield is greater than a preset digestion threshold, the digestion evaluation result is determined to meet the passage condition;

[0082] When the digestion yield is less than or equal to the preset digestion threshold, the digestion evaluation result is determined to be not meeting the subculture condition.

[0083] Preferably, the numerical range of the preset digestion threshold is: 95%-100%.

[0084] It can be understood that the digestion yield is the ratio of the number of cells in suspension obtained by digestion to the total number of cells in the cell culture container where the adherent cells are located; wherein the total number of cells is the sum of the number of cells in suspension and the number of cells in adherent cells. 消化后的贴壁细胞÷S 消化前的贴壁细胞 ), and the digestibility was calculated; where S 消化前的贴壁细胞 The area of ​​attached cells before digestion is obtained in advance. The digestion yield can be reflected by the area of ​​attached cells remaining after digestion. The higher the digestion yield, the better the cell digestion of the attached cells. Under normal circumstances, the digestion yield is required to be above 95%, that is, after the digestion is completed, the area occupied by the cells still attached to the wall does not exceed 5% of the bottom area of ​​the cell culture container under observation under a microscope. Of course, the preset digestion threshold of 95% can also be adjusted according to actual conditions, and can be adjusted to 94%, 95.5%, etc., and the specific value of the preset digestion threshold is not limited here.

[0085] For example, the method for evaluating the digestion results of adherent cells provided in this embodiment is used to evaluate the following: Figure 2 After a series of processing, the image of adherent cells after digestion obtained that the area ratio of cells still in the adherent state after digestion was 2.22%. 1 minus 2.22% equals 97.78% (that is, the digestion rate is 97.78%). 97.78% is greater than 95%, indicating that the cells have strong vitality and meet the production requirements of cell subculture.

[0086] See also Figure 3 , is a schematic flow chart of a cell culture method provided in an embodiment of the present invention.

[0087] The cell culture method provided by the embodiment of the present invention comprises:

[0088] S21. During the process of culturing adherent cells, obtaining an image of the adherent cells before digestion;

[0089] S22, performing image processing on the image of the adherent cells before digestion to obtain the area of ​​the adherent cells before digestion;

[0090] S23, obtaining the bottom area of ​​the cell culture container where the adherent cells are located;

[0091] S24, calculating the ratio of the adherent cell area before digestion to the bottom area to obtain the degree of confluence;

[0092] S25, when the degree of confluence is within a preset degree of confluence range, performing cell digestion on the adherent cells in the cell culture container by enzyme digestion;

[0093] S26. During the process of performing cell digestion on the adherent cells in the cell culture container, based on any one of the methods for evaluating adherent cell digestion results described above, evaluating the adherent cell digestion results to obtain a digestion evaluation result;

[0094] S27, when the digestion evaluation result satisfies the subculture condition, adding a digestion stop solution into the cell culture container to stop the cell digestion, and repeatedly washing the cell culture container after stopping the cell digestion to obtain a cell suspension;

[0095] S28, centrifuging the cell suspension, and placing the cells precipitated by centrifugation in a new cell culture container for cell culture.

[0096] Optionally, the preset fusion degree range is 80%-90%.

[0097] As one of the preferred implementations, the image processing of the adherent cell image before digestion to obtain the adherent cell area before digestion specifically includes:

[0098] Inputting the image of adherent cells before digestion into a pre-trained deep learning model for image processing to obtain a mask image of adherent cells before digestion; wherein the deep learning model is trained based on the training adherent cell image and the corresponding mask image of adherent cells;

[0099] Based on the mask image of adherent cells before digestion, the area of ​​adherent cells before digestion was calculated.

[0100] Further, the calculating the area of ​​adherent cells before digestion based on the mask image of adherent cells before digestion comprises:

[0101] Performing image grayscale processing on the mask image of adherent cells before digestion to obtain a grayscale image of adherent cells before digestion;

[0102] The area of ​​the adherent cells before digestion is obtained by calculating the area of ​​the region in which the pixel grayscale value is greater than a preset grayscale threshold in the grayscale image of the adherent cells before digestion.

[0103] It should be noted that, in actual operation, the preset grayscale thresholds used to calculate the area of ​​adherent cells before digestion and the area of ​​adherent cells after digestion may be the same or different, and may be adjusted according to actual conditions.

[0104] The cell culture method provided in this embodiment is described below with a specific example:

[0105] 1. Take the cell culture container to be subcultured out of the incubator, place it on the stage of the inverted biological microscope, rotate the multi-hole converter, and select the 10X objective lens. While observing, change and select the appropriate eyepiece, and then adjust the light source, image distance, and observation field to obtain the clearest picture. Use the instrument imaging software to take pictures and save them. Figure 4 Images of adherent cells before digestion are shown.

[0106] 2. After obtaining the image of the adherent cells before digestion according to step 1, use an image processing algorithm or a deep learning algorithm model to perform image processing on the image of the adherent cells before digestion to obtain the area of ​​the adherent cells before digestion, and use an image processing method to obtain the area of ​​the bottom image of the cell culture container in which the adherent cells are cultured, that is, the bottom area of ​​the cell culture container; wherein the specific steps of image processing the image of the adherent cells before digestion are as follows: (1) use annotation software to annotate the image of the adherent cells for training; (2) annotate the adherent cells to obtain an annotated label file of the image of the adherent cells for training; (3) convert the label file corresponding to the image of the adherent cells for training into a mask image of the annotated adherent cells, that is, an adherent cell mask image; (4) after obtaining the adherent cell mask image corresponding to the image of the adherent cells for training, adjust the parameters of the model to match the training image; The method comprises the following steps: (1) training a pre-constructed deep learning model by using a plurality of the training adherent cell images and the adherent cell mask images corresponding to each of the training adherent cell images; (2) generating a trained deep learning model after the training is completed; (3) inputting the adherent cell images before digestion into the pre-trained deep learning model, using the model for inference, and obtaining the adherent cell mask image before digestion; (4) setting the adherent cell area as a non-zero value area, and setting other areas other than the adherent cells as zero value areas. After obtaining the adherent cell mask image before digestion, extracting a single-channel adherent cell image based on the non-zero value area and the zero value area of ​​the adherent cells using an image grayscale processing method, setting preset grayscale thresholds for the non-zero value area and the zero value area of ​​the adherent cells, and counting the adherent cell area corresponding to the non-zero value area to obtain the adherent cell area before digestion.

[0107] 3. According to the area of ​​attached cells before digestion obtained in step 2 and the bottom area of ​​the cell culture container, calculate the ratio of the area of ​​attached cells before digestion to the bottom area of ​​the cell culture container to obtain the fusion degree of the attached cells. When the fusion degree is greater than 80%, it means that the attached cells are ready for the next step; otherwise, continue to culture the adherent cells.

[0108] 4. When the degree of confluence is 80%-90%, place the cell culture container in a biosafety cabinet, pour out the culture solution and add an appropriate amount of PBS (phosphate buffered saline) to wash away the residual culture medium in the cell culture container. After discarding the washing solution, add an appropriate amount of trypsin (Trypsin-EDTA) to digest the cells until the cells are translucent and round under the microscope and float up when gently shaken. Place the cell culture container under the microscope again, adjust the field of view, and take pictures of the cells in the cell culture container to obtain the following Figure 2 Images of adherent cells after digestion are shown.

[0109] 5. Perform image processing on the image of the adherent cells after digestion to obtain the area of ​​the adherent cells after digestion; according to the first expression 1-(S 消化后的贴壁细胞 ÷S 细胞培养容器 ), and the digestibility was calculated; where S 消化后的贴壁细胞 is the adherent cell area after digestion, S 细胞培养容器 is the bottom area of ​​the cell culture container obtained in advance; or, according to the second expression 1-(S 消化后的贴壁细胞 ÷S 消化前的贴壁细胞 ), and the digestibility was calculated; where S 消化前的贴壁细胞 is the area of ​​adherent cells before digestion obtained in advance; the adherent cell digestion result is evaluated according to the digestion yield, and when the digestion yield is greater than the preset digestion threshold, the digestion evaluation result is determined to meet the subculture condition; when the digestion yield is less than or equal to the preset digestion threshold, the digestion evaluation result is determined to not meet the subculture condition.

[0110] 6. When the digestion evaluation result meets the conditions for subculture, add an equal volume of digestion stop solution to dilute the digestion, repeatedly rinse the remaining cells in the cell culture container with a pipette, collect the cell suspension and centrifuge it, and place the cells precipitated by centrifugation in a suitable culture system for continued culture.

[0111] See also Figure 5 , is a structural diagram of a terminal device provided by an embodiment of the present invention.

[0112] A terminal device provided by an embodiment of the present invention includes a processor 10, a memory 20, and a computer program stored in the memory 20 and configured to be executed by the processor 10. When the processor 10 executes the computer program, the evaluation method of the adherent cell digestion result as described in any of the above embodiments is implemented.

[0113] When the processor 10 executes the computer program, the steps in the above-mentioned method for evaluating adherent cell digestion results are implemented, for example Figure 1 Alternatively, when the processor 10 executes the computer program, the functions of each module / unit in the above cell culture method embodiment are realized, for example Figure 3 The functions of each module of the cell culture method are shown.

[0114] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 20 and executed by the processor 10 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0115] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but not limited to, a processor 10 and a memory 20. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0116] The processor 10 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, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 10 is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0117] The memory 20 can be used to store the computer program and / or module. The processor 10 implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory 20 and calling the data stored in the memory 20. The memory 20 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0118] Wherein, if the module / unit integrated in the terminal device 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 such an understanding, the present invention implements all or part of the processes 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 various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0119] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0120] In summary, the evaluation method, cell culture method and terminal device of the digestion result of adherent cells provided by the embodiments of the present invention obtain the image of adherent cells after digestion; perform image processing on the image of adherent cells after digestion to obtain the area of ​​adherent cells after digestion; calculate the digestion yield according to the area of ​​adherent cells after digestion and the bottom area of ​​the cell culture container obtained in advance; or calculate the digestion yield according to the area of ​​adherent cells after digestion and the area of ​​adherent cells before digestion obtained in advance; wherein the digestion yield is the ratio of the number of cells in a suspended state obtained by digestion in the cell culture container to the total number of cells; the digestion result of adherent cells is evaluated according to the digestion yield to obtain the digestion evaluation result. Therefore, the error caused by manually evaluating the digestion result of adherent cells can be avoided to improve the accuracy of the digestion evaluation result of adherent cells, thereby ensuring the cell viability of adherent cells during the culture process.

[0121] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating adherent cell digestion results, characterized in that: include: Acquire images of adherent cells before and after digestion; Performing image processing on the image of the adherent cells before digestion and the image of the adherent cells after digestion to obtain the area of ​​the adherent cells before digestion and the area of ​​the adherent cells after digestion; According to the expression 1-(S 消化后的贴壁细胞 ÷S 消化前的贴壁细胞 ), and the digestibility was calculated; where S 消化前的贴壁细胞 is the area of ​​attached cells before digestion, S 消化后的贴壁细胞 is the area of ​​adherent cells after digestion; wherein the digestion yield is the ratio of the number of suspended cells obtained by digestion in the cell culture container where the adherent cells are located to the total number of cells, and the total number of cells is the sum of the number of suspended cells and the number of adherent cells; The adherent cell digestion result is evaluated according to the digestion yield to obtain a digestion evaluation result; in, Performing image processing on the image of the adherent cells before digestion and the image of the adherent cells after digestion to obtain the area of ​​the adherent cells before digestion and the area of ​​the adherent cells after digestion, including: Inputting the image of the adherent cells before digestion and the image of the adherent cells after digestion into a pre-trained deep learning model for image processing to obtain a mask image of the adherent cells before digestion and a mask image of the adherent cells after digestion; and calculating the area of ​​the adherent cells before digestion and the area of ​​the adherent cells after digestion, respectively, based on the mask image of the adherent cells before digestion and the mask image of the adherent cells after digestion; The deep learning model is specifically trained in the following way: Acquire multiple images of adherent cells for training; Annotating the adherent cells in each of the adherent cell images for training to obtain a label file for each of the adherent cell images for training; Convert each of the label files into a corresponding adherent cell mask image; Training a pre-constructed deep learning model using a plurality of the training adherent cell images and an adherent cell mask image corresponding to each of the training adherent cell images to obtain a trained deep learning model; The step of evaluating the adherent cell digestion result according to the digestion yield to obtain a digestion evaluation result includes: When the digestion yield is greater than a preset digestion threshold, the digestion evaluation result is determined to meet the passage condition; When the digestion yield is less than or equal to the preset digestion threshold, determining that the digestion evaluation result does not meet the passage condition; Among them, the numerical range of the preset digestion threshold is: 95%-100%.

2. The method for evaluating adherent cell digestion results according to claim 1, characterized in that: The method of calculating the adherent cell area before digestion and the adherent cell area after digestion based on the adherent cell mask image before digestion and the adherent cell mask image after digestion, respectively, comprises: Performing image grayscale processing on the mask image of the adherent cells before digestion and the mask image of the adherent cells after digestion to obtain a grayscale image of the adherent cells before digestion and a grayscale image of the adherent cells after digestion; Calculate the area of ​​the region where the pixel grayscale value in the grayscale image of the digested adherent cells is greater than a first preset grayscale threshold to obtain the area of ​​the adherent cells after digestion; The area of ​​the adherent cells before digestion is obtained by calculating the area of ​​the region in which the pixel grayscale value is greater than the second preset grayscale threshold in the grayscale image of the adherent cells before digestion.

3. The method for evaluating adherent cell digestion results according to claim 2, characterized in that: Calculating the area of ​​the region in which the pixel grayscale value in the grayscale image of the digested adherent cells is greater than a first preset grayscale threshold to obtain the area of ​​the adherent cells after digestion, comprising: Setting a first preset grayscale threshold between a non-0 value region and a 0 value region in the grayscale image of adherent cells after digestion, dividing the region where the adherent cells are located into a non-0 value region, and dividing the region outside the adherent cells into a 0 value region, and calculating the area of ​​the non-0 value region in the grayscale image of adherent cells after digestion to obtain the area of ​​adherent cells after digestion; Calculating the area of ​​the region in which the pixel grayscale value in the grayscale image of the adherent cells before digestion is greater than the second preset grayscale threshold to obtain the area of ​​the adherent cells before digestion, comprising: A second preset grayscale threshold is set for the non-0 value area and the 0 value area in the grayscale image of the adherent cells before digestion, the area where the adherent cells are located is divided into a non-0 value area, and the area outside the adherent cells is divided into a 0 value area, and the area of ​​the non-0 value area in the grayscale image of the adherent cells before digestion is calculated to obtain the area of ​​the adherent cells before digestion.

4. A cell culture method, characterized in that: include: During the culture of adherent cells, obtain images of adherent cells before digestion; performing image processing on the image of adherent cells before digestion to obtain the area of ​​adherent cells before digestion; Obtain the bottom area of ​​the cell culture container where the adherent cells are located; Calculating the ratio of the adherent cell area before digestion to the bottom area to obtain the degree of confluence; When the degree of confluence is within a preset degree of confluence range, performing cell digestion on the adherent cells in the cell culture container by an enzyme digestion method; In the process of performing cell digestion on the adherent cells in the cell culture container, based on the evaluation method for adherent cell digestion results as described in any one of claims 1 to 3, the adherent cell digestion results are evaluated to obtain a digestion evaluation result; When the digestion evaluation result is that the subculture condition is met, adding a digestion stop solution to the cell culture container to stop the cell digestion, and repeatedly washing the cell culture container after stopping the cell digestion to obtain a cell suspension; The cell suspension is centrifuged, and the cells precipitated by centrifugation are placed in a cell culture container for cell culture.

5. A terminal device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the adherent cell digestion result as described in any one of claims 1 to 3 and / or the cell culture method as described in claim 4 are implemented.

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