Drug action analysis method based on tumor organ three-dimensional roughness characterization

Abstract

The invention discloses a drug action analysis method based on tumor organoid three-dimensional roughness characterization, and relates to the technical field of tumor organoid. According to the method, the original three-dimensional structure data of the tumor organoids are obtained through three-dimensional interference without fluorescent reagent labeling, and then the connected domain data corresponding to each organoid in the target organoid cluster are segmented and determined, so that the morphological parameters of each organoid are determined, according to the method, the morphological parameters of at least one of the entity volume, the cavity volume, the post-filling volume, the post-filling surface area, the longest axis, the shortest axis and the sphericity of each similar organ are determined through internal hole filling, and the surface of each similar organ after internal hole filling is smoothed; according to the volume change caused by the smooth processing relative to the filled volume, the morphological parameter of the roughness corresponding to each organoid is determined, the roughness can perform quantitative characterization on the organoid heterogeneity to a great extent, and the accuracy of the tumor organoid drug effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor organoids, and particularly relates to a method for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids. Background Art

[0002] Generally, tumor heterogeneity has an important impact on drug response. Existing studies on the heterogeneity of tumor organoids mostly focus on the cytogenetics or cell composition level, lacking the analysis of tumor morphological diversity. The analysis of tumor organoid morphological diversity may be limited to the capture of the three-dimensional spatial structure information of tumor organoids or the simulation of tumor morphological change models.

[0003] Common bright-field microscopes lack the ability to image the three-dimensional structure of tumor organoids, ignoring the complexity of three-dimensional spatial relationships, resulting in limitations in morphological analysis based on two-dimensional images. Three-dimensional simulation can obtain the evolution of the three-dimensional model of tumor organoids based on information such as cell division and conduct theoretical analysis, but it cannot analyze the actual morphology of tumor organoids. Three-dimensional imaging is a necessary condition for revealing the complex structure and morphological phenotypes of tumor organoids and proving that tumor organoids can truly reflect their corresponding organs in vivo. Among them, confocal imaging or multiphoton microscopy based on fluorescent reagent labeling can achieve the visualization of three-dimensional details of tumor organoids and is widely used in various biomedical research including organoids. However, the permeability of fluorescent reagents is limited, it is difficult to obtain the internal morphology of dense three-dimensional tissues over 50 microns, the permeated tissue differences are large, and the detection process of fluorescent reagent labeling is harmful to cells, with complex operations, making it difficult to conveniently and quickly obtain the accurate morphological phenotypes of tumor organoids for application in drug effect analysis.

[0004] Therefore, label-free three-dimensional structure imaging and morphological analysis of tumor organoids are important bases for exploring the relationship between the morphological dynamic evolution of tumor organoids and drug effects. However, existing morphological characterizations of tumor organoids usually include morphological parameters such as volume and surface area, which have weak quantitative characterization of the heterogeneity of tumor organoids, poor pertinence in drug effect analysis based on existing tumor organoid morphological analysis, and poor accuracy in the correlation analysis between the morphological dynamic evolution of tumor organoids and drug effects. Summary of the Invention

[0005] Based on this, it is necessary to provide a method for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids in view of the above technical problems.

[0006] The present invention adopts the following technical solutions: The present invention provides a method for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids. First, according to the three-dimensional interference spectral data of multiple tumor organoids, the original three-dimensional structure data of multiple tumor organoids is obtained. Then, organoid segmentation and connectivity analysis are performed on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid in the target organoid cluster among multiple tumor organoids. After that, based on internal hole filling according to the connectivity domain data corresponding to each organoid in the target organoid cluster, morphological parameters including at least one of the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity of each organoid are determined, and the surface of each organoid after internal hole filling is smoothed. According to the volume change brought by the smoothing process relative to the filled volume, the morphological parameters corresponding to the roughness of each organoid are determined. Finally, morphological phenotype classification is performed on each organoid in the target organoid cluster according to the morphological parameters of each organoid, and a growth level model of each organoid in the target organoid cluster is constructed for drug effect analysis.

[0007] The present invention provides a device for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids, including: An acquisition module, configured to obtain the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectral data of multiple tumor organoids; A division module, configured to perform organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid in the target organoid cluster among multiple tumor organoids; A filling module, configured to determine the morphological parameters including the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity information of each organoid based on internal hole filling according to the connectivity domain data corresponding to each organoid in the target organoid cluster; A smoothing module, configured to smooth the surface of each organoid after internal hole filling, and determine the morphological parameters corresponding to the roughness of each organoid according to the volume change brought by the smoothing process relative to the filled volume; An analysis module, configured to perform morphological phenotype classification on each organoid in the target organoid cluster according to the morphological parameters of each organoid, and construct a growth level model of each organoid in the target organoid cluster for drug effect analysis.

[0008] The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids described above is implemented.

[0009] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method for analyzing the effect of a drug based on the three-dimensional roughness characterization of tumor organoids is realized.

[0010] The above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects: The present invention first obtains the original three-dimensional structure data of tumor organoids through three-dimensional interference without fluorescence reagent labeling, and then determines the connected domain data corresponding to each organoid in the target organoid cluster by segmentation, so as to determine the morphological parameters of each organoid. Among them, the present invention determines the morphological parameters including at least one of the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity of each organoid by filling internal holes, and smooths the surface of each organoid after filling the internal holes. Then, according to the volume change brought by the smoothing process relative to the filled volume, the morphological parameters of the roughness corresponding to each organoid are determined. The roughness obtained in this way can quantitatively characterize the heterogeneity of tumor organoids to a great extent. Subsequently, morphological phenotype classification and drug effect analysis are carried out based on the morphological parameters, which enhances the pertinence of the drug effect analysis of tumor organoids and improves the accuracy of the correlation analysis between the morphological dynamic evolution of tumor organoids and the drug effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 is a schematic flow chart of a method for analyzing the effect of a drug based on the three-dimensional roughness characterization of tumor organoids provided by the present invention; Figure 2 is a schematic diagram for determining the morphological parameters of the roughness of organoids provided by the present invention; Figure 3 is a schematic diagram for calculating the roughness of a simple three-dimensional body provided by the present invention; Figure 4 is a schematic diagram for calculating the roughness of a burr sphere provided by the present invention; Figure 5 is a schematic diagram for calculating the roughness of organoids provided by the present invention; Figure 6 is another schematic diagram for calculating the roughness of organoids provided by the present invention; Figure 7 is a schematic diagram of a device for analyzing the effect of a drug based on the three-dimensional roughness characterization of tumor organoids provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0014] Currently, it is difficult to obtain accurate morphological phenotypes of tumor organoids for drug action analysis based on the three-dimensional detail visualization of organoids achieved by confocal imaging or multiphoton microscopy techniques using fluorescent reagent labeling.

[0015] Therefore, there is an urgent need for a method that can perform high-throughput, longitudinal, label-free, three-dimensional imaging of organoids in a short time, and then use the morphological phenotypes of tumor organoids to describe the growth level, drug action, or invasiveness of tumor organoids.

[0016] The following will describe in detail the technical solutions provided by each embodiment of the present invention in conjunction with the drawings.

[0017] Figure 1 It is a schematic flow diagram of a drug action analysis method based on the three-dimensional roughness characterization of tumor organoids in the present invention, specifically including the following steps: S101: Obtain the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectral data of multiple tumor organoids.

[0018] S102: Perform organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid in the target organoid cluster among multiple tumor organoids.

[0019] S103: Based on internal hole filling according to the connectivity domain data corresponding to each organoid in the target organoid cluster, determine the morphological parameters of each organoid including at least one of the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity.

[0020] S104: Smooth the surface of each organoid after internal hole filling, and determine the morphological parameter of the roughness corresponding to each organoid according to the volume change caused by the smoothing relative to the filled volume.

[0021] S105: Classify the morphological phenotypes of the organoids in the target organoid cluster according to the morphological parameters of each organoid, and construct a growth level model of the organoids in the target organoid cluster for drug action analysis.

[0022] For the convenience of description, only the server will be used as the execution entity for illustration below. The server mentioned in the present invention may be a server set up on a service platform, or a device such as a desktop computer or a laptop computer that can execute the solution of the present invention.

[0023] Generally, when analyzing the effect of a drug based on the morphological phenotype of tumor organoids, the server may first obtain the three-dimensional interference spectral data of multiple tumor organoids under normal culture conditions based on three-dimensional optical imaging technology .

[0024] Then, the three-dimensional interference spectral data is processed by wavenumber resampling, DC removal, Fourier transform, etc. to perform the conversion from the frequency domain to the depth domain, so as to obtain the original three-dimensional structure data of multiple tumor organoids .

[0025] Next, the original three-dimensional structure data is segmented for organoids to obtain the binary data containing the target organoid cluster in multiple tumor organoids .

[0026] Thus, the binary data containing the target organoid cluster is subjected to connectivity analysis to obtain the number of individual organoids within the target organoid cluster N and the connectivity domain data corresponding to each organoid within the target organoid cluster .

[0027] Next, morphological analysis can be performed on each organoid to obtain its multi-dimensional morphological parameters and determine the morphological phenotype of each organoid.

[0028] Based on this, phenotypic classification can be performed on various organoids, such as kmeans clustering analysis based on machine learning, so as to perform statistical analysis on each organoid by category, and construct a linear model representing the growth levels of various organoids within the target organoid cluster. Finally, the growth level changes and drug action effects of various organoids within the target organoid cluster at different times can be evaluated based on the constructed linear model.

[0029] Among them, in one or more embodiments of the present invention, when the server performs morphological analysis on each organoid to obtain its multi-dimensional morphological parameters, it may first perform internal hole filling on the three-dimensional image containing only a single organoid , and then calculate the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity information of the single organoid respectively based on the three-dimensional image after internal hole filling .

[0030] For the roughness of each organoid, refer to Figure 2 , Figure 2 which is a schematic diagram for determining the morphological parameters of the roughness of organoids in the present invention.

[0031] The server can first perform three-dimensional Fourier transform and spectral centering on each organoid after filling the internal holes to obtain the frequency spectrum of each organoid , and determine the original power spectrum of the three-dimensional image of each organoid according to the frequency spectrum of each organoid .

[0032] Then, by using low-pass frequency domain filtering based on the power spectrum ratio with different cut-off frequencies, the surface of each organoid after filling the internal holes is smoothed to obtain the filtered frequency spectrum diagram and determine the power spectrum retained after filtering.

[0033] Specifically, when the server smooths a single organoid after filling the internal holes , the smoothing method can be selected as low-pass frequency domain filtering based on the power spectrum ratio, such as Butterworth low-pass filtering with a cut-off frequency of : .

[0034] The server can select different cut-off frequencies , and multiply with the corresponding filter and to obtain the filtered frequency spectrum diagram , and calculate the power spectrum retained by the filtered image .

[0035] Subsequently, the server can determine the preferred cut-off frequency according to the power spectrum ratio (only for illustrative purposes here, and other values can be determined according to needs), to ensure the smoothness between different organoids. And perform Fourier transform and binarization on the smoothed frequency spectrum diagram corresponding to the preferred cut-off frequency to determine the smoothed data of each organoid under the corresponding power spectrum ratio . .

[0036] Finally, the server can perform exclusive OR processing on the data before smoothing after filling the internal holes of each organoid and the smoothed data of each organoid . The exclusive OR result represents the volume change after smoothing of each organoid . The volume change after smoothing of each organoid can be determined according to and the ratio of the surface area before smoothing after filling the internal holes of each organoid ​ , determine the morphological parameters corresponding to the roughness of each organoid. The above process of determining roughness can obtain the accurate roughness of tumor organoids without labeling.

[0037] Figure 3 This is a schematic diagram of the roughness calculation of a simple three-dimensional object in the present invention. Figure 3 On the left side is the initial simple three-dimensional object, and its surface area is 0.099 mm 2 ; when the optimal power spectrum ratio is selected as 90%, the Figure 3 filtered result in the middle can be obtained, and its volume change is 0.00000875 mm 3 , and the roughness calculation result is 0.000088 mm; when the optimal power spectrum ratio is selected as 85%, the Figure 3 filtered result on the right can be obtained, and its volume change is 0.00003900 mm 3 , and the roughness calculation result is 0.000394 mm.

[0038] Figure 4 This is a schematic diagram of the roughness calculation of a burr ball in the present invention. Figure 4 On the left side is the initial burr ball with a radius of 100. When the optimal power spectrum ratio is selected as 85%, the Figure 4 filtered result on the right can be obtained, and the volume change after filtering is 0.01460750 mm 3 , and the roughness calculation result is 0.004875 mm.

[0039] Figure 5 This is a schematic diagram of the roughness calculation of an organoid in the present invention. Figure 5 On the left side is the initial organoid obtained after segmentation. When the power spectrum ratio is 90%, the Figure 5 filtered result of the organoid in the middle can be obtained, and the volume change before and after filtering is 0.00114262 mm 3 , and the roughness calculation result is 0.001423 mm; when the power spectrum ratio is 85%, the Figure 5 filtered result of the organoid on the right can be obtained, and the volume change before and after filtering is 0.00290750 mm 3 , and the roughness is 0.003621 mm.

[0040] Figure 6 This is another schematic diagram of the roughness calculation of an organoid in the present invention. Figure 6 On the left side is another initial organoid obtained after segmentation. When the power spectrum ratio is 90%, the Figure 6 filtered result of the organoid in the middle can be obtained, and the volume change before and after filtering is 0.00048875 mm 3, the roughness calculation result is 0.001361 mm; when the power spectrum accounts for 85%, the Figure 5 filtered result of the organoids on the right side can be obtained, and the volume change before and after filtering is 0.00068650 mm 3 , and the roughness is 0.001912 mm.

[0041] In addition, in one or more embodiments of the present invention, when the server performs phenotype classification on various organoids in the target organoid cluster, clustering analysis methods including but not limited to kmeans clustering, DBSCAN clustering, etc. can be used.

[0042] Taking the improved Kmeans clustering as an example, the server can first perform data normalization and correlation analysis on multi-dimensional morphological parameters, and determine and remove redundant features in the morphological parameters according to the correlation threshold. Specifically, the server can perform data normalization and correlation analysis on the entity volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, sphericity, and roughness parameters obtained in the previous steps respectively, and remove redundant features with a correlation ≥ 95%.

[0043] Then, the server can perform balance processing on the data of each type of organoid in the target organoid cluster, and based on the preferred number of clusters and preferred organoids as the initial clustering centers, perform clustering analysis according to the morphological parameters of each organoid through Kmeans clustering to determine the classification of each organoid in the target organoid cluster.

[0044] For the number of clusters and clustering centers, the morphological characteristics of organoids can be analyzed based on existing data, and the optimal number of clusters P , and select representative organoids for each category as the initial clustering centers.

[0045] For the balance processing, there are mainly two means. Sample imbalance processing 1: When the proportion of the number of organoids smaller than the size threshold in the target organoid cluster is greater than the first threshold, downsampling processing is performed on the part of the organoids. That is, considering that the organoids in the organoid cluster have different sizes and the proportion of the number of small-sized organoids is significantly larger, downsampling processing is performed on the small-sized organoids.

[0046] Sample imbalance processing 2: When the proportion of the number of organoids of any category in the target organoid cluster is less than the second threshold and the importance is greater than the importance threshold, the organoids of this category are classified separately. That is, considering that the organoids in the organoid cluster have different sizes, if the proportion of the number of organoids of a certain category is extremely small but its existence is extremely important, such as large-sized cavity organoids, then the organoids of this category need to be manually grouped into one category.

[0047] Perform Kmeans clustering on the organoids after sample imbalance processing, finally obtain the clustering model, and get P class mean vectors. Subsequently, the above clustering model can be used to perform clustering analysis on newly collected organoid data to obtain the category information of individual organoids.

[0048] Further, in one or more embodiments of the present invention, when the server constructs the growth level model of each type of organoid in the target organoid cluster, the server can, for each type of organoid in the target organoid cluster, statistically calculate the mean, median, and standard deviation of different morphological parameters of this type of organoid, so as to standardize the morphological parameters of this type of organoid; specifically, according to the organoid data of each category in the target organoid cluster, obtain the number of organoids in each category, as well as the mean, median, and standard deviation and other information of various morphological parameters such as the physical volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, sphericity, and roughness.

[0049] After that, the server can perform a correlation analysis between the number of each type of organoid and the characteristics of different morphological parameters, and determine and remove the redundant characteristics of each type of organoid according to the correlation threshold. For example, redundant characteristics with a correlation ≥ 95% can be removed.

[0050] Then, perform eigenvalue decomposition on the correlation coefficient matrix of the remaining characteristics of each type of organoid to obtain multiple eigenvalues and corresponding eigenvectors for principal component analysis, and determine the eigenvectors corresponding to the eigenvalues that reach the preset cumulative contribution rate as the principal components. Usually, according to the size of the eigenvalues, select the eigenvectors corresponding to the first p largest eigenvalues as the principal components. Generally, eigenvalues with a cumulative contribution rate reaching a certain proportion (such as 85%) can be selected.

[0051] Finally, the selected eigenvectors can be used to calculate the scores of each type of organoid community on these principal components. These scores constitute the new data set after dimensionality reduction. Thus, determine the scores of this type of organoid on each principal component, weight and sum the corresponding scores through the contribution rates of each principal component, and determine the growth level of each type of organoid.

[0052] Through correlation analysis, it can be found that there is a certain correlation between multiple morphological parameters of different organoid communities. Therefore, the method of principal component analysis can be applied to reduce the dimension of the morphological parameters of multiple categories to simplify the data structure and improve the accuracy and interpretability of the comprehensive evaluation of the growth level of the organoid community.

[0053] Finally, the drug effect analysis of tumor organoids can be performed according to the growth levels under different drugs or drug concentrations. There are already relatively mature technologies for performing drug effect analysis of tumor organoids based on growth levels, and the present invention will not elaborate on this.

[0054] Based on Figure 1The drug action analysis method based on the three-dimensional roughness characterization of tumor organoids. In the present invention, the original three-dimensional structure data of tumor organoids is first obtained through three-dimensional interference without fluorescence reagent labeling, and then the connected domain data corresponding to each organoid within the target organoid cluster is determined by segmentation, so as to determine the morphological parameters of each organoid. Among them, in the present invention, at least one of the morphological parameters including the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity of each organoid is determined by internal hole filling, and the surface of each organoid after internal hole filling is smoothed. Then, according to the volume change brought by the smoothing process relative to the filled volume, the morphological parameters corresponding to the roughness of each organoid are determined. The roughness obtained in this way can greatly quantify and characterize the organoid heterogeneity. Subsequently, morphological phenotype classification and drug action analysis are carried out based on the morphological parameters, which enhances the pertinence of the drug action analysis of tumor organoids and improves the accuracy of the correlation analysis between the morphological dynamic evolution of tumor organoids and drug action.

[0055] When applying the drug action analysis method based on the three-dimensional roughness characterization of tumor organoids provided by the present invention, it is not necessary to execute according to Figure 1 the order of the steps shown. The specific execution order of each step can be determined according to needs, and the present invention does not limit this.

[0056] The above is the drug action analysis method based on the three-dimensional roughness characterization of tumor organoids provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding drug action analysis device based on the three-dimensional roughness characterization of tumor organoids, as Figure 7 shown.

[0057] Figure 7 Schematic diagram of a drug action analysis device based on the three-dimensional roughness characterization of tumor organoids provided by the present invention, including: An acquisition module 201, configured to obtain the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectrum data of the multiple tumor organoids; A division module 202, configured to perform organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connected domain data corresponding to each organoid within the target organoid cluster among the multiple tumor organoids; A filling module 203, configured to determine at least one of the morphological parameters including the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity information of each organoid based on internal hole filling according to the connected domain data corresponding to each organoid within the target organoid cluster; A smoothing module 204, configured to smooth the surface of each organoid after internal hole filling, and determine the morphological parameters corresponding to the roughness of each organoid according to the volume change brought by the smoothing process relative to the filled volume; An analysis module 205, configured to classify the morphological phenotypes of various organoids within a target organoid cluster according to the morphological parameters of each organoid, and construct a growth level model of various organoids within the target organoid cluster for drug effect analysis.

[0058] For the specific limitations of the drug effect analysis device based on the three-dimensional roughness characterization of tumor organoids, reference can be made to the limitations of the drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids in the above text, which will not be elaborated here. Each module in the above drug effect analysis device based on the three-dimensional roughness characterization of tumor organoids can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0059] The present invention also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 provided drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids.

[0060] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 provided drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above various methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0062] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded by the present invention.

Claims

1. A method for analyzing the effect of drugs based on the three-dimensional roughness characterization of tumor organoids, characterized in that Including: Obtain the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectral data of the multiple tumor organoids; Perform organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid in the target organoid cluster among the multiple tumor organoids; Based on internal hole filling according to the connectivity domain data corresponding to each organoid in the target organoid cluster, determine the morphological parameters of each organoid including at least one of the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity; Smooth the surface of each organoid after internal hole filling, and determine the morphological parameter of the roughness corresponding to each organoid according to the volume change brought about by the smoothing relative to the filled volume; Classify the morphological phenotypes of the organoids in the target organoid cluster according to the morphological parameters of each organoid, and construct a growth level model of the organoids in the target organoid cluster for drug action analysis.

2. The method for analyzing the effect of a drug based on the three-dimensional roughness characterization of tumor organoids according to claim 1, wherein The smoothing the surface of each organoid after internal hole filling, and determining the morphological parameter of the roughness corresponding to each organoid according to the volume change brought about by the smoothing relative to the filled volume specifically includes: Perform three-dimensional Fourier transform and spectral centering on the organoids after internal hole filling to obtain the frequency spectra of the organoids to determine the original power spectrum of each organoid; Smooth the surface of each organoid after internal hole filling by using different cut-off frequencies through low-pass frequency domain filtering based on the power spectrum ratio, obtain the filtered spectrogram and determine the power spectrum retained after filtering; Determine the preferred cut-off frequency according to the power spectrum ratio, perform Fourier transform and binarization on the smoothed spectrogram corresponding to the preferred cut-off frequency to determine the smoothed data of each organoid under the corresponding power spectrum ratio; Perform exclusive OR processing on the data before smoothing after internal hole filling of each organoid and the smoothed data of each organoid to determine the volume change after smoothing of each organoid; Determine the morphological parameter of the roughness corresponding to each organoid according to the ratio of the volume change after smoothing of each organoid to the surface area before smoothing after internal hole filling of each organoid.

3. The drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids according to claim 1, wherein, The obtaining the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectral data of the multiple tumor organoids specifically includes: Obtain the three-dimensional interference spectral data of multiple tumor organoids based on three-dimensional optical imaging technology; Perform conversion from the frequency domain to the depth domain on the three-dimensional interference spectral data to obtain the original three-dimensional structure data of multiple tumor organoids.

4. The drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids according to claim 1, wherein The performing organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid in the target organoid cluster among the multiple tumor organoids specifically includes: Perform organoid segmentation on the original three-dimensional structure data to obtain the binary data including the target organoid cluster among the multiple tumor organoids; Perform connectivity analysis on the binary data including the target organoid cluster to obtain the connectivity domain data corresponding to each organoid in the target organoid cluster.

5. The drug effect analysis method based on the three-dimensional roughness characterization of tumor organoids according to claim 1, characterized in that, The classifying the morphological phenotypes according to the morphological parameters of each organoid in the target organoid cluster specifically includes: Normalize and perform correlation analysis on the morphological parameters of each organoid within the target organoid cluster, and determine and remove redundant features in the morphological parameters according to the correlation threshold; Balance the data of each type of organoid within the target organoid cluster, and based on the preferred number of clusters and the preferred organoids as the initial clustering centers, perform clustering analysis according to the morphological parameters of each organoid through Kmeans clustering to determine the classification of each organoid within the target organoid cluster.

6. The method for analyzing drug effects based on the three-dimensional roughness characterization of tumor organoids according to claim 5, wherein The balance processing of the data of each type of organoid within the target organoid cluster specifically includes: When the proportion of the number of organoids smaller than the size threshold within the target organoid cluster is greater than the first threshold, perform downsampling on some of the organoids; When the proportion of the number of organoids of any category within the target organoid cluster is less than the second threshold and the importance is greater than the importance threshold, perform separate classification or oversampling on the organoids of this category.

7. The method for analyzing the effect of a drug based on the three-dimensional roughness characterization of tumor organoids according to claim 1, wherein The construction of the growth level model of each type of organoid within the target organoid cluster specifically includes: For each type of organoid within the target organoid cluster, count the number of organoids of this type and the mean, median, and standard deviation of different morphological parameters of the organoids of this type, and standardize the morphological parameters of the organoids of this type; Perform correlation analysis on the features between the number of organoids of each type and different morphological parameters, and determine and remove redundant features of each type of organoid according to the correlation threshold; Perform eigenvalue decomposition on the correlation coefficient matrix of the remaining features of each type of organoid to obtain multiple eigenvalues and corresponding eigenvectors for principal component analysis, and determine the eigenvectors corresponding to the eigenvalues that reach the preset cumulative contribution rate as the principal components; Determine the scores of each type of organoid on each principal component, weight and sum the corresponding scores through the contribution rate of each principal component, and determine the growth level of each type of organoid.

8. A drug action analysis device based on the three-dimensional roughness characterization of tumor organoids, characterized in that, Including: An acquisition module for acquiring the original three-dimensional structure data of multiple tumor organoids according to the three-dimensional interference spectral data of multiple tumor organoids; A division module for performing organoid segmentation and connectivity analysis on the original three-dimensional structure data to determine the connectivity domain data corresponding to each organoid within the target organoid cluster among multiple tumor organoids; A filling module for determining the morphological parameters including the solid volume, cavity volume, filled volume, filled surface area, longest axis, shortest axis, and sphericity information of each organoid based on internal hole filling according to the connectivity domain data corresponding to each organoid within the target organoid cluster; A smoothing module for smoothing the surface of each organoid after internal hole filling, and determining the morphological parameter corresponding to the roughness of each organoid according to the volume change brought by the smoothing process relative to the filled volume; An analysis module for performing morphological phenotype classification on each type of organoid within the target organoid cluster according to the morphological parameters of each organoid, and constructing a growth level model of each type of organoid within the target organoid cluster for drug action analysis.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method according to any one of claims 1 to 7.

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