Method, device and system for evaluating inter-chip splicing precision of biological tissue imaging, medium and product

Abstract

The invention relates to a method, a device and a system for evaluating the inter-slice splicing precision of biological tissue imaging, a medium and a product. The method comprises the following steps: acquiring a plurality of groups of imaging data obtained by imaging a plurality of slice objects of a tissue sample by imaging equipment; performing three-dimensional image reconstruction on the multiple groups of imaging data by using a to-be-evaluated three-dimensional image reconstruction method to obtain a reconstructed image; comparing the sizes of corresponding parts in the reconstructed image and the reference image in the stacking direction of the plurality of slice objects to obtain a comparison result; and based on the comparison result, evaluating the inter-slice splicing precision of the plurality of slice objects by the three-dimensional image reconstruction method. According to the method and the device, the problem that an evaluation scheme for the inter-slice splicing precision of the three-dimensional image reconstruction algorithm is lacked can be solved, and the inter-slice splicing precision of the three-dimensional image reconstruction algorithm can be accurately evaluated.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular, to a method, apparatus, system, medium, and product for evaluating the inter-slice stitching accuracy of biological tissue imaging. Background Art

[0002] With the development of image processing technology, methods for processing image data have been widely applied in various fields, especially for post-processing the imaging data collected by devices. Taking the biomedical field as an example, biomedical imaging technology plays an important role in disease prevention, diagnosis, treatment, and monitoring, and is also a key tool for in-depth exploration of biological tissue research. In particular, the spatial morphology, connection relationships, etc. that can be presented by tissue-intact three-dimensional imaging, such as in brain science, imaging and three-dimensional image reconstruction technology have promoted the in-depth understanding of brain structure, brain function, and brain connection complexity.

[0003] With the continuous progress of imaging technology, optical coherence tomography (OCT) and polarization-sensitive optical coherence tomography (PSOCT) that combines polarization detection technology, as a high-resolution, non-contact three-dimensional imaging technology, bring great convenience to the exploration of biological tissues.

[0004] However, when imaging using such three-dimensional imaging technology, due to the limited imaging depth, it is usually necessary to perform slicing operations on the object to be imaged, obtain the imaging data of multiple slicing operations, and perform three-dimensional image reconstruction on these data to restore the three-dimensional reconstructed image of the object to be imaged. In this regard, the accuracy of the three-dimensional image reconstruction algorithm is closely related to the accuracy and reliability of the three-dimensional reconstructed image. However, there is currently a lack of a method for evaluating the depth stitching accuracy of the three-dimensional image reconstruction algorithm, and it may be difficult to ensure that the accuracy and reliability of the three-dimensional reconstructed image are not impaired due to image stitching errors. Summary of the Invention

[0005] The present disclosure provides a method, apparatus, system, medium, and product for evaluating the inter-slice stitching accuracy of biological tissue imaging to at least solve the problem in the related art that there is a lack of an evaluation scheme for the inter-slice stitching accuracy of the three-dimensional image reconstruction algorithm. The technical solutions of the present disclosure are as follows: According to a first aspect of the present disclosure, there is provided a method for evaluating the inter-slice stitching accuracy of biological tissue imaging. The evaluation method includes: obtaining multiple sets of imaging data obtained by an imaging device imaging multiple slice objects of a tissue sample, where the multiple slice objects are multiple tissue slices obtained by performing multiple slicing operations on the tissue sample or multiple cross-sections obtained by performing multiple slicing operations on the tissue sample, the multiple sets of imaging data correspond to the multiple slice objects one by one, and each set of imaging data includes at least one imaging data of the corresponding slice object; using a three-dimensional image reconstruction method to be evaluated to perform three-dimensional image reconstruction on the multiple sets of imaging data to obtain a reconstructed image; comparing the sizes of corresponding parts in the reconstructed image and a reference image in the stacking direction of the multiple slice objects to obtain a comparison result, where the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample; and evaluating the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the multiple slice objects based on the comparison result.

[0006] Optionally, the comparing the sizes of corresponding parts in the reconstructed image and a reference image in the stacking direction of the multiple slice objects to obtain a comparison result includes: determining cross-sectional images corresponding to each other in the reconstructed image and the reference image, where the tissue cross-sections included in the cross-sectional images are parallel to the stacking direction; respectively determining the maximum sizes of the tissue cross-sections in the stacking direction in the corresponding cross-sectional images; and comparing the maximum sizes of the corresponding cross-sectional images to obtain the comparison result.

[0007] Optionally, there are multiple tissue samples, and the comparison result includes a result corresponding to each tissue sample. Wherein, the evaluating the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the multiple slice objects based on the comparison result includes: determining a statistical value of all comparison results; and determining the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the multiple slice objects based on the statistical value and the number of slicing operations performed on each tissue sample.

[0008] Optionally, the imaging data is obtained by the following method: determining the number of slicing operations according to the size of the tissue sample; slicing the tissue sample according to the number of slicing operations in a fixed field of view of the imaging device, and after each slicing operation, using the imaging device to perform at least one imaging on the current slice object to obtain imaging data corresponding to each slicing operation.

[0009] Optionally, the using the biological tissue imaging device to perform at least one imaging on the current slice object includes: using the imaging device to perform at least one imaging on the current slice object until imaging data of all positions in the current slice object is obtained.

[0010] Optionally, the at least one imaging of the current slice object using the imaging device includes: performing multiple imagings of the current slice object using the imaging device according to the imaging field of view of the imaging device, a preset planar imaging redundancy overlap amount, and a preset imaging sequence, where the planar imaging redundancy overlap amount represents the overlap amount between the imaging regions of two adjacent imagings when performing multiple imagings of the same slice object. Wherein, the three-dimensional reconstruction of the multiple groups of imaging data using the three-dimensional image reconstruction method to be evaluated to obtain a reconstructed image includes: performing planar stitching on each group of imaging data to obtain a planar stitched image corresponding to each group of imaging data; based on each planar stitched image, the slice thickness of the slice, the imaging depth of the imaging device, and the depth imaging redundancy overlap amount, using the three-dimensional image reconstruction method to stitch the planar stitched images in the stacking direction to obtain the reconstructed image.

[0011] According to a second aspect of the present disclosure, there is provided an evaluation device for the inter-slice stitching accuracy of biological tissue imaging. The evaluation device includes: an acquisition unit configured to acquire multiple groups of imaging data obtained by imaging a plurality of slice objects of a tissue sample using an imaging device, where the plurality of slice objects are a plurality of tissue slices obtained by performing multiple slicing operations on the tissue sample or a plurality of cross-sections obtained by performing multiple slicing operations on the tissue sample, the multiple groups of imaging data corresponding to the plurality of slice objects one by one, and each group of imaging data includes at least one imaging data of the corresponding slice object; a reconstruction unit configured to perform three-dimensional image reconstruction on the multiple groups of imaging data using a three-dimensional image reconstruction method to be evaluated to obtain a reconstructed image; a comparison unit configured to compare the sizes of corresponding parts in the stacking direction of the multiple slice objects in the reconstructed image and a reference image to obtain a comparison result, where the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample; and an evaluation unit configured to evaluate the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the plurality of slice objects based on the comparison result.

[0012] According to a third aspect of the present disclosure, there is provided an evaluation system for the inter-slice stitching accuracy of biological tissue imaging. The evaluation system includes a movable stage, a slicer, and a computing device. The computing device receives imaging data obtained by imaging a tissue sample placed on the movable stage using an imaging device. The slicer is used to perform slicing operations on the tissue sample. The computing device includes a processor and a memory for storing instructions executable by the processor. When the instructions executable by the processor are run by the processor, the processor is caused to execute the evaluation method for the inter-slice stitching accuracy of biological tissue imaging according to the present disclosure.

[0013] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by a processor of a computing device, enables the computing device to execute the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to the present disclosure.

[0014] According to a fifth aspect of the present disclosure, there is provided a computer program product, which includes computer-executable instructions that, when executed by at least one processor, implement the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to the present disclosure.

[0015] The technical solution provided by the present disclosure at least brings the following beneficial effects: According to the present disclosure, multiple sets of imaging data obtained by imaging a plurality of slice objects of a tissue sample by a biological tissue imaging device can be acquired, and a three-dimensional image reconstruction can be performed by using a three-dimensional image reconstruction method to obtain a reconstructed image. The sizes of corresponding parts in the reconstructed image and a reference image in the stacking direction of the slice objects can be compared, and based on the comparison result, the accuracy of the inter-slice splicing of the slice objects by the three-dimensional image reconstruction method can be evaluated. In this way, the inter-slice splicing accuracy of the three-dimensional image reconstruction method can be accurately evaluated, which helps to ensure that the accuracy and reliability of the reconstructed three-dimensional image are not impaired due to image splicing errors, and provides guidance for subsequent processing or application of the three-dimensional reconstructed image.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0018] Figure 1 is a schematic flowchart of a method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0019] Figure 2 is a schematic diagram of an example of a system for evaluating the inter-slice splicing accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0020] Figure 3 and Figure 4 is a schematic diagram of the imaging of multiple consecutive positions in a single cross-section of a tissue sample obtained in the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0021] Figure 5It is a schematic diagram of a three-dimensional reconstruction image of consecutive cross-sections of a mouse brain tissue stitched using a three-dimensional image reconstruction method in an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0022] Figure 6 It is a schematic diagram of a non-destructive MRI imaging system for obtaining a tissue sample in an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0023] Figure 7 It is an example diagram of a non-destructive three-dimensional image of a mouse brain tissue obtained using a magnetic resonance imaging device in an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0024] Figure 8 It is a schematic flowchart of a step for comparing a reconstructed image with a reference image in an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0025] Figure 9 They are respectively schematic diagrams of geometric dimensions of non-destructive MRI mouse imaging and three-dimensional reconstruction images of PSOCT imaging measured using an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0026] Figure 10 It is a schematic diagram of a measurement size list and inter-slice stitching accuracy of a slice layer of a three-dimensional reconstruction algorithm in an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0027] Figure 11 It is a schematic flowchart of an example of an evaluation method for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0028] Figure 12 It is a schematic block diagram of an evaluation system device for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure.

[0029] Figure 13 It is a schematic block diagram of an evaluation system for inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure. Detailed implementation manners

[0030] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0032] It should be noted that the phrase "at least one of the items" in the present disclosure includes three types of parallel situations: "any one of the items", "a combination of any number of the items", and "all of the items". For example, "including at least one of A and B" includes the following three types of parallel situations: (1) including A; (2) including B; (3) including A and B. Another example is "executing at least one of step 1 and step 2" which means the following three types of parallel situations: (1) executing step 1; (2) executing step 2; (3) executing step 1 and step 2.

[0033] As mentioned above, in the related art, there is a lack of methods for evaluating the depth stitching accuracy of the three-dimensional image reconstruction algorithm, which may make it difficult to ensure the accuracy and reliability of the three-dimensional reconstructed image.

[0034] Specifically, in traditional methods, the microscopic structure of tissues is usually observed by slicing biological tissues, staining the slices, and then imaging them. However, this imaging method not only has high professional technical requirements for the operators of slicing and staining, but also takes a long time to produce and has high labor costs. More importantly, this imaging method can only obtain biological information that is sensitive to specific dyes, and it is very easy to have incomplete slicing during the production process, and the slices are broken during staining and mounting, resulting in partial damage or loss of tissues, making it impossible to obtain comprehensive information about the tissue.

[0035] Optical coherence tomography (OCT) and its polarization-sensitive optical coherence tomography (PSOCT) combined with polarization detection technology are high-resolution, non-contact three-dimensional imaging technologies that greatly facilitate the exploration of biological tissues. However, the imaging field of view (FOV) and imaging depth of this type of biological tissue imaging are limited, and it is difficult to show the full picture of the tissue at high resolution through a single imaging.

[0036] In some cases, embedding materials can be used to support biological tissues, and biological tissue sectioning techniques and three-dimensional reconstruction of images can be applied to tissue imaging, so that three-dimensional volume imaging of tissues can be successfully obtained. However, during the entire imaging process, due to the diversity of biological tissue components, plastic changes after specimen processing, and the accuracy limitation of the moving stage in the depth direction, there may be differences in section thickness during the continuous sectioning process, which may lead to errors in the splicing between each section during the three-dimensional reconstruction of the image. The existence of these errors may cause key information loss or image overlap during the further analysis of the overall connection relationship, structural characteristics, and functions of biological tissues, which may lead to a series of problems, including misjudgment of the lesion area in biomedical imaging, distortion of the geometric structure of the reconstructed model, etc., restricting the depth and breadth of related research. Therefore, it is very necessary to quantitatively evaluate the splicing accuracy between sections in three-dimensional image reconstruction.

[0037] In view of the above problems, an exemplary embodiment of the present disclosure provides a method, apparatus, and system for evaluating the splicing accuracy between sections in biological tissue imaging, a computer-readable storage medium, and a computer program product, which can solve or at least alleviate the above problems.

[0038] In the first aspect of the exemplary embodiment of the present disclosure, a method for evaluating the splicing accuracy between sections in biological tissue imaging is provided.

[0039] The method for evaluating the splicing accuracy between sections in biological tissue imaging according to the exemplary embodiment of the present disclosure can be applied to a scenario where a user interacts with software. For example, software can be loaded on a user terminal, and the user can input an evaluation instruction for the three-dimensional image reconstruction method to be evaluated on the user terminal. The user terminal can evaluate the splicing accuracy between sections of the three-dimensional image reconstruction method by executing the method for evaluating the splicing accuracy between sections in biological tissue imaging according to the exemplary embodiment of the present disclosure.

[0040] Specifically, the user terminal can obtain multiple sets of imaging data obtained by cross-sectional imaging of multiple sections of a tissue sample by a biological tissue imaging device. Among them, the multiple section objects are multiple tissue sections obtained by performing multiple sectioning operations on the tissue sample or multiple cross-sections obtained by performing multiple sectioning operations on the tissue sample. The multiple sets of imaging data correspond to the multiple section objects one by one, and each set of imaging data includes at least one imaging data of the corresponding section object. The user terminal can use the three-dimensional image reconstruction method to be evaluated to perform three-dimensional image reconstruction on the multiple sets of imaging data to obtain a reconstructed image. The user terminal can compare the sizes of the corresponding parts in the stacking direction of the multiple section objects in the reconstructed image and a reference image, where the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample, to obtain a comparison result. The user terminal can evaluate the splicing accuracy between sections of the three-dimensional image reconstruction method for the multiple section objects based on the comparison result.

[0041] The above user terminal can be, for example, a workstation, a laptop computer, etc. However, the implementation scenario of the above method is only an example scenario. The method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the exemplary embodiments of the present disclosure can also be applied to other application scenarios. For example, it can also be that the user requests the evaluation of the inter-slice stitching accuracy from the server through the network on the user terminal (such as a mobile phone, a desktop computer, a tablet computer, etc.). The server can complete the request by executing the method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the exemplary embodiments of the present disclosure. Here, the server can be an independent server, a server cluster, a cloud computing platform, or a virtualization center.

[0042] According to the method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the exemplary embodiments of the present disclosure, based on the principle of geometric dimension measurement, it can quickly and effectively quantitatively evaluate the inter-slice stitching accuracy in the three-dimensional reconstruction after tissue sectioning, provide accurate feedback for optimizing the three-dimensional image reconstruction algorithm, and also provide a quantitative basis for adjusting the sectioning and imaging parameters.

[0043] As Figure 1 shown, the method for evaluating the inter-slice stitching accuracy of biological tissue imaging may include the following steps: In step S110, multiple sets of imaging data obtained by the imaging device for cross-sectional imaging of multiple slices of the tissue sample can be acquired.

[0044] Here, the imaging device can be, for example, but not limited to, optical microscopes such as OCT, PSOCT, electron microscopes, etc. It can be used, for example, in the biomedical field to image biological tissue sections. And because the imaging field of view and imaging depth of such an imaging device are limited, when acquiring a three-dimensional image, it is necessary to first image multiple slice objects and then reconstruct and restore the three-dimensional image through an algorithm. And because the number of slice objects is large, especially for large-sized tissues to be imaged, if the inter-slice stitching accuracy of the three-dimensional image reconstruction method in the depth direction is not high, relatively large errors may be introduced during the reconstruction process, thus affecting the reliability of the three-dimensional reconstructed image. The evaluation method of the embodiments of the present disclosure can better handle the evaluation of the stitching accuracy of multi-slice imaging three-dimensional reconstruction of the imaging device. In addition, the tissue sample can be, for example, an ex vivo tissue or organ of a non-living animal, a local tissue of a plant body, etc.

[0045] In this step, the multiple slice objects can be multiple tissue sections obtained by performing multiple slicing operations on the tissue sample (i.e., the tissue parts cut off by the slicing operation) or multiple cross-sections obtained by performing multiple slicing operations on the tissue sample (i.e., the cross-sections of the remaining tissue parts after the slicing operation). The multiple sets of imaging data can correspond to the multiple slice objects one by one, and each set of imaging data can include at least one imaging data of the corresponding slice object.

[0046] Specifically, in one example, multiple slicing operations can be performed on a tissue sample, and after each slicing operation, the sliced section is removed. The imaging device is used to image the cross-section of the remaining part of the tissue sample. This process can be repeated multiple times until all the sliced cross-section imaging data of at least a part of the tissue sample in the depth direction is obtained. In another example, multiple slicing operations can be performed on a tissue sample, and after each slicing operation, the imaging device is used to image the sliced section. This process can be repeated multiple times until all the tissue section imaging data of at least a part of the tissue sample in the depth direction is obtained. Here, in the example where the slicing object is a tissue section, due to the slicing operation or the tissue section transfer operation after the slicing operation, the sliced section itself may be deformed, etc. In the example using the sliced cross-section, the sliced cross-section can be imaged without transfer and without deformation, reducing the introduction of errors.

[0047] For example, the above operation process can be implemented by Figure 2 the evaluation system for the inter-slice stitching accuracy of biological tissue imaging according to the embodiments of the present disclosure shown. Specifically, as Figure 2 shown, the system 200 may include a moving stage 210, a slicer 220, and a computing device 230. Among them, the slicer 220 can perform slicing operations on the tissue sample 20, for example, it can be a vibratome; the moving stage 210 can be located below the imaging device 10 and is movable, for example, it can move in the vertical direction and two mutually perpendicular horizontal directions, so as to drive the tissue sample 20 to move relative to the imaging device 10. Here, the moving stage 210 can be a stage capable of precise movement, which may include a stage control system (not shown). The imaging device 10 may include an imaging controller 11, a reference arm 12, and an imaging head 13. The computing device 230 can be used to execute the evaluation method described herein. The computing device 230 can communicate bidirectionally with the imaging device 10 and the stage control system, for example, it can monitor the imaging trigger signal, imaging position information, imaging parameters, and imaging status. In addition, the computing device 230 is adapted to the communication methods supported by the actually used imaging device 10 and the stage control system.

[0048] In addition, the system may further include a server 240, which is communicatively connected to the computing device 230. For example, the server 240 can be used to store multiple imaging data.

[0049] As an example, taking the whole-brain imaging of a mouse as an example, 10% gelatin can be used to embed the brain tissue of the mouse, and then 4% formaldehyde can be used to fix the embedded sample to increase the cross-linking degree between the tissue specimen and the embedding acquisition, completing the sample preparation. Then, the sample can be fixed to the moving stage 210 (which can also be called the sample placement stage) in the experimental pool using an adhesive such as 502 glue. For example, for each section object, imaging data of multiple consecutive positions of the biological tissue can be obtained to complete the imaging of each section object, and the position of the moving stage 210 in the depth direction can be adjusted according to the section thickness for subsequent sectioning, so as to obtain the imaging of all consecutive sections of the tissue.

[0050] Although the above describes an example implementation of acquiring imaging data, the embodiments of the present disclosure are not limited thereto, and the acquisition of multiple imaging data can also be implemented through other systems or structures.

[0051] As an example, the imaging data in step S110 can be obtained in the following manner: determine the number of sectioning times according to the size of the tissue sample; according to the number of sectioning times, perform sectioning operations on the tissue sample according to the number of sectioning times under the fixed field of view of the imaging device, and after each sectioning operation, use the imaging device to perform at least one imaging on the current section object obtained by the current sectioning operation to obtain imaging data corresponding to each sectioning operation. In this way, imaging data of multiple section objects can be obtained without moving the imaging device, which is more convenient for operation and control.

[0052] Specifically, the imaging range of a single plane and the number of sectioning times can be determined according to the geometric size of the tissue. Here, the section thickness can be determined according to the imaging depth of the imaging device. For example, the section thickness can be less than or equal to the imaging depth of the imaging device. In the case of determining the section thickness, the number of sectioning times in the depth direction (or the direction perpendicular to the section object or the section stacking direction) can be determined.

[0053] In addition, as an example, when imaging each section object, by moving the tissue sample in the horizontal plane, the section objects with the section objects are respectively moved to different imaging positions to obtain imaging data corresponding to multiple imaging positions, forming a set of imaging data, and during the process of acquiring these imaging data, continuous position imaging with a certain degree of redundant overlap can be performed on the tissue sample. For example, the imaging data can be acquired by using the multiple consecutive block imaging (Black-face imaging) method.

[0054] For example, the step of using the imaging device to perform at least one imaging on the current section object can include: performing multiple imagings on the current section object by using the imaging device according to the imaging field of view of the imaging device, the preset redundant overlap amount of plane imaging, and the preset imaging sequence.

[0055] Here, the planar imaging redundant overlap amount can represent the overlap amount between the imaging regions of two adjacent imagings when imaging the same slice object multiple times. Both the planar imaging redundant overlap amount and the imaging order can be set according to actual needs. As an example, the imaging order within a single plane can be determined according to the planar stitching algorithm described later. For example, the imaging order can match the stitching process of the planar stitching algorithm, and according to the input quantity requirements of the planar stitching algorithm, determine this imaging order. For example, when the algorithm performs image stitching in the order from left to right and from top to bottom, the imaging can be carried out in the same or opposite order as this order.

[0056] As an example, in step S110, the imaging and slicing parameters can be set by using a parameter configuration module provided in the computing device or controller. Specifically, on the one hand, for imaging within a single plane (or a single slice object), the imaging field of view, imaging order, and planar imaging redundant overlap amount need to be set, and the corresponding in-plane imaging position coordinates are generated according to these imaging parameters. On the other hand, the imaging position coordinates in the depth direction perpendicular to the slice object can be determined according to the slice thickness, the imaging depth of the imaging device, and the depth imaging redundant overlap amount in the depth direction. Specifically, the imaging field of view, target image acquisition resolution, and imaging range of the tissue to be measured in the used imaging system can be set, the imaging field of view, planar imaging redundant overlap amount, slice thickness, depth imaging redundant overlap amount, and total number of imagings are configured, and based on the imaging redundant overlap amount and the total number of imagings, the imaging position coordinates within a single plane and in the depth direction are calculated.

[0057] For example, the imaging field of view size of the biological tissue imaging device can be set to 3 mm; the planar imaging redundant overlap amount of a single slice object can be set to 20%; the depth imaging redundant overlap amount in the depth direction can be set to 50%; the slice thickness can be set to 0.2 mm. In this example, according to the configured imaging and slicing parameters, the number of imaging data of the imaging device for each slice object and the position of each imaging can be determined. For example, the imaging position coordinates of multiple consecutive imagings within a single imaging plane can be generated according to the set planar imaging redundant overlap amount and imaging field of view.

[0058] According to the parameters set above, continuous tomographic (C-scan) imaging of multiple (such as 20) positions can be carried out in a column scan order with priority in the Y direction at scanning positions of 5 per row and 4 per column, and the imaging of each position on the horizontal plane (such as the XY plane) can be obtained. For example, as Figure 3 shown in Figure 3 is a plan view (i.e., XY plane view) showing the imaging of a single slice object at each position.

[0059] After imaging a single slice plane, for example, the stage can be moved to the position at the time of slicing (e.g., the position where the slicer is located), and the stage is moved up by the slice thickness (e.g., 0.2 mm) to cut off the current slice object that has been imaged, and the above process is repeated to complete the C-scan imaging of multiple (e.g., 20) positions of the new slice object.

[0060] As an example, a predetermined number of the above processes of slicing and imaging the cross-section after slicing can be performed on the tissue sample, or until the last slice of the tissue sample is sliced. Thus, the process of collecting multi-group imaging data is completed.

[0061] Although it is described above that multiple imaging data can be collected for each slice object, the embodiments of the present disclosure are not limited thereto, and only one imaging data can also be collected for any slice object.

[0062] In addition, according to the embodiments of the present disclosure, the step of imaging the current slice object at least once using the imaging device may include: imaging the current slice object at least once using the imaging device until imaging data for all positions in the current slice object are obtained.

[0063] Specifically, since the reconstructed image needs to be compared with the reference image subsequently, in order to facilitate determining the correspondence between the reconstructed image and the reference image, whether multiple imaging data are collected for each slice object or only one imaging data is collected, the stitched image of each slice object (e.g., in the example where each group of imaging data includes multiple imaging data) or the image (e.g., in the example where each group of imaging data includes one imaging data) can include the complete slice object. Thus, after reconstruction, the tissue sample included in the reconstructed image has a complete tissue morphology in the stacking direction of the slice objects, which is beneficial for subsequent algorithms or manual identification of the positional correspondence between the reconstructed image and the reference image. However, the embodiments of the present disclosure are not limited thereto, and imaging data for a preset area of each slice object can also be obtained only.

[0064] In step S120, a three-dimensional image reconstruction method to be evaluated can be used to perform three-dimensional image reconstruction on the multi-group imaging data to obtain a reconstructed image.

[0065] In this step, the three-dimensional image reconstruction method to be evaluated can be any three-dimensional image reconstruction method, as long as it can perform inter-slice stitching in the depth direction of the slices, this method can be used for evaluation.

[0066] As an example, data calculation can be performed on the obtained multi-group imaging data, and in the case where each group of imaging data includes multiple imaging data, plane image stitching can also be performed on the multiple imaging data of each slice object.

[0067] In this example, step S120 may include: performing planar stitching on each set of imaging data to obtain a planar stitched image corresponding to each set of imaging data; and performing stitching on the planar stitched images in the stacking direction by using a three-dimensional image reconstruction method based on the planar stitched images, the slice thickness of the slices, the imaging depth of the imaging device, and the depth imaging redundancy overlap amount, to obtain a reconstructed image.

[0068] As an example, the imaging data of each slice object may be stitched according to the imaging order, imaging field of view, and planar imaging redundancy overlap amount within a single plane during the imaging process. Here, for example, the above-mentioned planar stitching may be performed by using a known imaging stitching algorithm for a single plane. Figure 4 An example of a PSOCT stitched image of a single slice object is shown.

[0069] In the case of obtaining the planar stitched images corresponding to each slice object, the planar stitched images may be stitched in the stacking direction by using a three-dimensional image reconstruction algorithm to be evaluated, so that a reconstructed image can be obtained.

[0070] As an example, the stitching redundancy amount between slices during image three-dimensional reconstruction may be calculated according to the slice thickness and imaging depth during the imaging process, and the three-dimensional reconstruction of all slice imaging may be realized by using the three-dimensional image reconstruction algorithm to be evaluated based on all the planar stitched images. Here, the stitching redundancy amount between slices represents the overlap amount between the imaging data of two adjacent slice objects in the stacking direction of the slice objects. The imaging depth may refer to the distance that the imaging device can image in the depth direction or the thickness direction when imaging a single slice object. In the embodiments of the present disclosure, considering that there is light attenuation in the imaging depth during a single imaging, the slice thickness of each slice operation may be smaller than the single imaging depth, so that the stitching redundancy amount between slices can be formed.

[0071] Figure 5 An example reconstructed image of the three-dimensional body reconstruction of all tissue imaging completed by using the three-dimensional image reconstruction algorithm to be measured after stitching the PSOCT stitched images of all slice objects is shown. Here, since there is a certain degree of attenuation in the depth direction when light passes through the tissue during PSOCT imaging and no light attenuation correction is performed in the three-dimensional reconstruction algorithm, Figure 5 the reconstructed image in [reference] shows differences in gray-scale contrast display and stitching traces in the depth direction.

[0072] In the above manner, by performing planar stitching on multiple imaging data, local image information can be effectively integrated to form a more comprehensive image, which helps to improve the resolution of the finally obtained three-dimensional reconstructed image.

[0073] In addition, although it is described here that a separate imaging stitching algorithm can be used to perform planar stitching on multiple imaging data of each slice object to obtain a planar stitched image, the embodiments of the present disclosure are not limited thereto. For example, the algorithm part of planar image stitching can also be included in the three-dimensional image reconstruction method to be evaluated. In this way, a planar stitched image can be obtained using the three-dimensional image reconstruction method, and a reconstructed image can be further obtained.

[0074] In step S130, the sizes of corresponding parts in the stacking direction of multiple slice objects in the reconstructed image and the reference image can be compared to obtain a comparison result.

[0075] Here, the reference image can be obtained by performing non-destructive three-dimensional imaging on the tissue sample. For example, it can be magnetic resonance (Magnetic Resonance Imaging, MRI) imaging, computed tomography (Computed Tomography, CT) imaging, etc. The reference image obtained by non-destructive three-dimensional imaging can be used as the true value of the size of the tissue sample for comparison with the reconstructed image.

[0076] As an example, when obtaining the reference image, as Figure 6 shown, a Bruker BioSpec 94 / 30 small animal MRI imaging system can be used, and the imaging parameters are set as TR = 2000 ms, TE = 20 ms, Averages = 4, FOV = 12x11x17 mm 3 , where TR represents the repetition time, TE represents the echo time, and mm represents the millimeter unit. In this way, non-destructive three-dimensional MRI imaging of the mouse brain tissue can be achieved. Figure 7 Shows an example of a non-destructive three-dimensional image of the mouse brain tissue obtained using an MRI imaging device.

[0077] Here, since it is difficult for the imaging angles of the same slice object in the MRI imaging system and a biological tissue imaging system such as PSOCT imaging to be exactly the same, in the evaluation method of the embodiments of the present disclosure, differences in the display effects of the reference image and the reconstructed image for slice objects parallel to the stacking direction (such as the XZ plane) are allowed (such as the image display effect differences shown in Figure 5 and Figure 7 ). Even with such differences, the method of the embodiments of the present disclosure can still effectively evaluate the inter-slice stitching accuracy of the three-dimensional image reconstruction method.

[0078] Although the above description is given by taking MRI imaging as an example, the embodiments of the present disclosure are not limited thereto. The imaging technology for collecting non-destructive three-dimensional imaging of the tissue sample can also be other imaging technologies as long as three-dimensional volume imaging of the tissue sample can be achieved.

[0079] In this step S130, the corresponding parts in the reconstructed image and the reference image in the stacking direction of multiple slice objects can be any position or region of the tissue sample. The sizes of the corresponding parts in the two images can be compared, for example, by comparing the difference or ratio between the sizes of the corresponding parts, to determine the inter-slice stitching accuracy of the three-dimensional image reconstruction method.

[0080] As an example, as Figure 8 shown, this step S130 may include the following steps: In step S810, the cross-sectional images corresponding to each other in the reconstructed image and the reference image can be determined.

[0081] Here, the tissue cross-sections included in the cross-sectional images can be parallel to the stacking direction. For example, multiple pairs of candidate cross-sectional image pairs corresponding to each other in the stacking direction can be obtained from the reconstructed image and the reference image respectively, and the similarity between each pair of candidate cross-sectional image pairs can be determined. The candidate cross-sectional image pair with the maximum similarity is used as the cross-sectional images corresponding to each other in this step. Here, the similarity can be calculated, for example, by using existing image processing methods, such as determining the similarity by calculating the image distance (such as Euclidean distance, etc.) between the two images.

[0082] In step S820, in the cross-sectional images corresponding to each other, the maximum sizes of the tissue cross-sections in the stacking direction can be determined respectively.

[0083] Here, the cross-sectional images corresponding to each other determined in the above steps can be measured for size. In one example, the two cross-sectional images can be input into a medical image software such as ITK-SNAP, and the "Line and Ruler Mode" of the tool "Annotation Inspector" in the software can be used to measure the maximum size of the tissue cross-section in the stacking direction in each cross-sectional image. For example, as Figure 9 shown, the two positions with the farthest distance in the stacking direction of the tissue cross-section can be marked in each cross-sectional image, such as A1 and B1 in the MRI image and A2 and B2 in the reconstructed PSOCT image. Based on the two positions with the farthest distance, the straight-line distances of A1~B1 and A2~B2 can be measured by the software, and the straight-line distance is used as the maximum size of the tissue cross-section in the stacking direction.

[0084] In step S830, the maximum sizes of the cross-sectional images corresponding to each other can be compared to obtain a comparison result.

[0085] In the case of determining the above maximum sizes, the comparison result can be obtained by means such as difference comparison, ratio comparison, percentage comparison, etc.

[0086] For example, the comparison result can be represented by the following formula (1): (1) Among them, the reference measurement value represents the maximum size of the tissue cross-section in the stacking direction in the cross-sectional image corresponding to the reference image (such as an MRI image), and the three-dimensional reconstruction measurement value represents the maximum size of the tissue cross-section in the stacking direction in the cross-sectional image corresponding to the reconstructed image.

[0087] In the above method, by determining the maximum size of the tissue cross-section in the stacking direction, the difference between the two cross-sectional images can be globally evaluated. The comparison result obtained in this way is more representative, making the subsequent evaluation of the inter-slice splicing accuracy more accurate. However, the embodiments of the present disclosure are not limited thereto, and other parts or other sizes in the two cross-sectional images can also be selected for comparison.

[0088] In addition, in the above example, the operation of geometric dimension measurement involves the geometric dimension measurement in the three-dimensional reconstruction image after imaging the reference image as a non-destructive three-dimensional imaging and the slice object of continuous slices. Among them, the measurement direction of the geometric dimension in the reference image is consistent with the three-dimensional reconstruction direction between slices in the reconstructed image.

[0089] In step S140, based on the comparison result, the accuracy of the three-dimensional image reconstruction method for inter-slice splicing of multiple slice objects can be evaluated.

[0090] In this step, based on the geometric dimensions measured from the reference image and the reconstructed image, the accuracy of the three-dimensional image reconstruction method for inter-slice splicing of multiple slice objects can be determined. Here, the accuracy of inter-slice splicing of multiple slice objects refers to the splicing accuracy between adjacent slice objects.

[0091] In one example, the operations of the above steps S110 to S130 can be performed on a single tissue sample to obtain a comparison result corresponding to the tissue sample for calculating the inter-slice splicing accuracy. For example, the percentage between the above dimensions measured in the reference image and the reconstructed image can be used as the inter-slice splicing accuracy.

[0092] In another example, in order to reduce the influence of errors in the manual measurement process and tissue individual differences on the splicing accuracy between slice objects, the operations of the above steps S110 to S130 can be performed on multiple tissue samples to obtain corresponding comparison results respectively, and then the accuracy evaluation can be performed based on all the comparison results.

[0093] Specifically, there can be multiple tissue samples, and the comparison results can include results corresponding to each tissue sample. This step S140 can include: determining the statistical value of all comparison results; and determining the splicing accuracy of the inter-slice of multiple slice objects by the three-dimensional image reconstruction method based on the statistical value and the number of slicing operations performed on each tissue sample.

[0094] For example, the operations of the above steps S110 to S130 can be performed on the brain tissue samples of 9 mice to compare the geometric dimensions of the reference images and three-dimensional reconstructed images of each tissue sample in the stacking direction, and the measured geometric dimensions of these 9 tissue samples can be statistically obtained to obtain Figure 10 the measurement size list shown in.

[0095] As an example, the statistical value of all comparison results can be determined by the following formula (2): (2) Wherein, represents the statistical value of all comparison results, represents the number of tissue samples (9 in the above example), represents the i th comparison result corresponding to the tissue sample, for example, it can be determined based on the above formula (1). can characterize the error in size between the reference image and the reconstructed image. For example, as shown in Figure 10 , column D in the list represents the error values corresponding to each tissue sample .

[0096] Based on the above formula (2) and the number of slicing operations performed on each tissue sample, the splicing accuracy of the three-dimensional image reconstruction method for the slice objects can be determined as shown in the following formula (3): (3) Wherein, represents the splicing accuracy of the three-dimensional image reconstruction method for the slice objects, and N 切片 represents the number of slicing operations for each tissue sample. Taking the above example, when the average number of slices of 9 tissue samples to be measured is N 切片 = 75, .

[0097] Although the above example is described with the average value as an example, the statistical value of the comparison results can also be other values, such as the mode, median, variance, etc.

[0098] Regarding the problem of the impact of the splicing accuracy between slices on the accuracy, reliability, and credibility in the in-depth analysis of image data during the three-dimensional image reconstruction process, according to an exemplary embodiment of the present disclosure, an evaluation method for the inter-slice splicing accuracy of biological tissue imaging is provided, which is used to optimize the image splicing algorithm between slices in the three-dimensional reconstruction of tissue section imaging, and particularly relates to the application scenario of obtaining overall high-resolution imaging by continuously slicing biological tissue multiple times.

[0099] In addition, the above evaluation method can calculate the splicing accuracy between slices in the three-dimensional reconstruction by comparing the geometric dimensions in the depth direction of the non-destructive three-dimensional imaging of the tissue and the three-dimensional reconstructed image of the slices. Its measurement principle is simple, and the operation process is clear and convenient. In addition, using the quantitative evaluation value can provide accurate quantitative feedback data for the three-dimensional reconstruction algorithm to be measured.

[0100] Using this method can not only significantly improve the optimization efficiency of the three-dimensional reconstruction algorithm, improve the quality of image splicing, provide data guarantee for in-depth analysis of high resolution, but also provide a quantitative reference for tissue embedding, adjusting slice parameters, and imaging parameters.

[0101] Figure 11 An example of the evaluation method for the inter-slice splicing accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure is shown. In this example, the slice section is taken as an example of the slice object for description.

[0102] In step S1101, a reference image of the non-destructive three-dimensional imaging of the tissue sample, such as an MRI image, can be obtained. In step S1102, sample preparation before slice imaging of the tissue sample can be performed.

[0103] After the sample preparation is completed, in step S1103, the slice imaging software can be started, and in step S1104, imaging parameters and slice parameters can be set, such as including but not limited to imaging field of view, imaging redundant overlap, imaging range, imaging times, etc.; slice thickness, slice speed, etc.

[0104] In step S1105, coordinates of multiple imaging positions to which the tissue sample will continuously move can be generated according to the imaging range and imaging parameters. In step S1106, imaging of the tissue sample can be started according to the multiple imaging positions of the current section.

[0105] Specifically, in step S1107, the tissue sample can be moved to the current imaging position, and in step S1108, it can be determined whether the current imaging is completed. In response to completing the current imaging, in step S1109, it can be further determined whether all imaging in the current column is completed; in response to not completing the current imaging, step S1108 can be executed again.

[0106] In response to completing the imaging of all positions in the current column, at step S1110, it can be further determined whether the imaging of all positions in the current section is completed. In response to completing the imaging of all positions in the current section, at step S1111, the section that has completed imaging can be cut off, for example, sliced according to a preset slice thickness. In response to not completing the imaging of all positions in the current section, it can return to step S1107, and use the next imaging position as the current imaging position for this imaging.

[0107] At step S1112, it can be determined whether the imaging of all sections is completed. In response to completing the imaging of all sections, at step S1113, the image data can be solved for subsequent processing. In response to not completing the imaging of all sections, it can return to step S1106 to image the current section.

[0108] At step S1114, the images of a single section can be stitched to obtain a planar stitched image of each section. At step S1115, based on a three-dimensional image reconstruction algorithm to be evaluated, the planar stitched images of all sections can be stitched in the stacking direction to obtain a reconstructed image.

[0109] At step S1116, the true value in the reference image can be measured, such as the geometric size of the non-destructive imaging in the depth direction. And at step S1117, the measured value in the three-dimensional reconstructed image of the section imaging can be measured, such as the geometric size of the reconstructed image in the depth stitching direction.

[0110] At step S1118, the true value and the measured value can be compared to obtain a comparison result, and based on the comparison result, the accuracy measurement of the three-dimensional image reconstruction algorithm can be obtained.

[0111] The method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to an exemplary embodiment of the present disclosure relates to the field of image processing, and particularly also relates to the three-dimensional reconstruction of imaging data of multiple slice object fields after biological tissue slicing in the field of biomedicine. Among them, tissue imaging is the basis for microscopic structure observation and research. For tissues with sizes exceeding the single imaging field of view and imaging depth, it is difficult to obtain complete imaging information through a single imaging. Therefore, continuous slicing of biological tissues and obtaining the imaging data of the slice objects, and three-dimensional reconstruction of the imaging data of these slice objects are important technical means to obtain complete imaging of the whole tissue. High-precision three-dimensional image reconstruction is crucial for revealing the overall morphology, spatial connection relationship, etc. of biological tissues.

[0112] In this regard, the method for evaluating the inter-slice stitching accuracy of biological tissue imaging provided by the exemplary embodiments of the present disclosure can quantify the inter-slice stitching accuracy, visually present the specific parameters that need to be optimized for the reconstruction algorithm to be tested, and thus provide more reliable data guarantee for the research on the structural features and functional associations of biological tissues. It can also provide accurate feedback for adjusting the slicing and imaging parameters to a certain extent.

[0113] In a second aspect of the exemplary embodiments of the present disclosure, an apparatus for evaluating the inter-slice stitching accuracy of biological tissue imaging is provided. As Figure 12 shown, the evaluation apparatus includes an acquisition unit 1210, a reconstruction unit 1220, a comparison unit 1230, and an evaluation unit 1240.

[0114] The acquisition unit 1210 is configured to acquire multiple sets of imaging data obtained by sectional imaging of multiple slices of a tissue sample by an imaging device. Among them, the multiple slice objects are multiple tissue slices obtained by performing multiple slicing operations on the tissue sample or multiple cross-sections obtained by performing multiple slicing operations on the tissue sample. The multiple sets of imaging data correspond one-to-one to the multiple slice objects, and each set of imaging data includes at least one imaging data of the corresponding slice object.

[0115] The reconstruction unit 1220 is configured to perform three-dimensional image reconstruction on the multiple sets of imaging data by using a three-dimensional image reconstruction method to be evaluated, and obtain a reconstructed image.

[0116] The comparison unit 1230 is configured to compare the sizes of corresponding parts in the reconstructed image and the reference image in the stacking direction of the multiple slice objects, and obtain a comparison result. Among them, the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample.

[0117] The evaluation unit 1240 is configured to evaluate the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the multiple slice objects based on the comparison result.

[0118] As an example, the comparison unit 1230 is configured to: determine cross-sectional images corresponding to each other in the reconstructed image and the reference image, where the tissue cross-sections included in the cross-sectional images are parallel to the stacking direction; in the cross-sectional images corresponding to each other, respectively determine the maximum sizes of the tissue cross-sections in the stacking direction; compare the maximum sizes of the cross-sectional images corresponding to each other to obtain a comparison result.

[0119] As an example, there are multiple tissue samples, and the comparison result includes the result corresponding to each tissue sample. Among them, the comparison unit 1230 is configured to: determine the statistical value of all comparison results; based on the statistical value and the number of slicing operations performed on each tissue sample, determine the inter-slice stitching accuracy of the three-dimensional image reconstruction method for the multiple slices.

[0120] As an example, the imaging data is obtained as follows: according to the size of the tissue sample, the number of slicing times is determined; according to the number of slicing times, the tissue sample is sliced according to the number of slicing times under the fixed field of view of the biological tissue imaging device, and after each slicing operation, the biological tissue imaging device is used to perform at least one imaging on the cross-section of the current slice to obtain the imaging data corresponding to each slicing operation.

[0121] As an example, using the imaging device to perform at least one imaging on the cross-section of the current slice includes: using the imaging device to perform at least one imaging on the current slice object obtained by the current slicing operation until the imaging data of all positions in the cross-section of the current slice is obtained.

[0122] As an example, using the imaging device to perform at least one imaging on the cross-section of the current slice includes: according to the imaging field of view of the imaging device, the preset planar imaging redundancy overlap amount, and the preset imaging sequence, using the imaging device to perform multiple imagings on the current slice object, where the planar imaging redundancy overlap amount represents the overlap amount between the imaging regions of two adjacent imagings when performing multiple imagings on the same slice object. Among them, the reconstruction unit 1220 is configured to: perform planar stitching on each set of imaging data to obtain a planar stitched image corresponding to each set of imaging data; based on each planar stitched image, the slice thickness of the slice, the imaging depth of the imaging device, and the depth imaging redundancy overlap amount, use a three-dimensional image reconstruction method to stitch each planar stitched image in the stacking direction to obtain a reconstructed image.

[0123] Regarding the device in the above embodiments, the specific manner in which each unit performs operations has been described in detail in the embodiments related to the method. Each unit in the evaluation device for the inter-slice stitching accuracy of biological tissue imaging can execute the corresponding steps in the method according to the method for evaluating the inter-slice stitching accuracy of biological tissue imaging in the method embodiments of the first aspect above, and will not be elaborated here in detail.

[0124] In the third aspect of the exemplary embodiments of the present disclosure, an evaluation system for the inter-slice stitching accuracy of biological tissue imaging is provided. As Figure 13 shown, the evaluation system 1300 includes a mobile stage 1310, a slicer 1320, and a computing device 1330. The computing device 1330 receives the imaging data obtained by imaging the tissue sample placed on the mobile stage by the imaging device, and the slicer 1320 is used to slice the tissue sample. Here, the evaluation system 1300 may include an imaging device, and the imaging device may be disposed above the mobile stage 1310; the evaluation system 1300 may also not include an imaging device. For example, an external imaging device may also be connected to the evaluation system 1300 to implement the evaluation of the inter-slice stitching accuracy.

[0125] The computing device 1330 includes a processor and a memory for storing processor-executable instructions. When the processor-executable instructions are run by the processor, they cause the processor to execute a method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the embodiments of the present disclosure.

[0126] As an example, the evaluation system can be the evaluation system as Figure 2 shown, which has been described in detail above and will not be elaborated here.

[0127] As an example, the computing device 1330 does not have to be a single device and can also be any collection of devices or circuits that can execute the above instructions (or instruction sets) individually or jointly. The computing device 1330 can also be part of an integrated control system or system manager, or can be configured as a server that interfaces with a local or remote (e.g., via wireless transmission) network.

[0128] In the computing device 1330, the processor can include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, the processor can also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0129] The processor can run instructions or code stored in the memory, where the memory can also store data. The instructions and data can also be sent and received via a network interface device over a network, where the network interface device can use any known transmission protocol.

[0130] The memory can be integrated with the processor. For example, RAM or flash memory can be arranged within an integrated circuit microprocessor, etc. In addition, the memory can include separate devices, such as an external disk drive, a storage array, or other storage devices that can be used by any database system. The memory and the processor can be operatively coupled or can communicate with each other, for example, via an I / O port, a network connection, etc., such that the processor can read files stored in the memory.

[0131] In addition, the computing device 1330 can also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.). All components of the computing device 1340 can be connected to each other via a bus and / or a network.

[0132] In a fourth aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by the processor of a computing device, the computing device can execute a method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the embodiments of the present disclosure.

[0133] A computer-readable storage medium may, for example, be a memory including instructions. Optionally, the computer-readable storage medium may be: read-only memory (ROM), random access memory (RAM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disc memory, hard disk drive (HDD), solid state drive (SSD), cartridge memory (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and provide the computer program and any associated data, data files, and data structures to a processor or computer such that the processor or computer can execute the computer program. The computer program in the above computer-readable storage medium can run in an environment deployed in computer devices such as clients, hosts, proxy devices, servers, etc. In addition, in one example, the computer program and any associated data, data files, and data structures are distributed on a networked computer system such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.

[0134] In a fifth aspect of the exemplary embodiments of the present disclosure, there is provided a computer program product including computer-executable instructions that, when executed by at least one processor, implement the method for evaluating the inter-slice stitching accuracy of biological tissue imaging according to the embodiments of the present disclosure.

[0135] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0136] In addition, it should be noted that although several examples of each step are described above with reference to specific drawings, it should be understood that the embodiments of the present disclosure are not limited to the combinations given in the examples. Steps appearing in different drawings can be combined, and the execution order of each step can be changed, and no exhaustive list is made here.

[0137] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for evaluating the splicing accuracy between slices in biological tissue imaging, characterized in that, The evaluation method includes: Obtaining multiple sets of imaging data obtained by an imaging device imaging multiple slice objects of a tissue sample, where the multiple slice objects are multiple tissue sections obtained by performing multiple slicing operations on the tissue sample or multiple cross-sections obtained by performing multiple slicing operations on the tissue sample, the multiple sets of imaging data correspond one-to-one with the multiple slice objects, and each set of imaging data includes at least one imaging data of the corresponding slice object; Using a three-dimensional image reconstruction method to be evaluated, performing three-dimensional image reconstruction on the multiple sets of imaging data to obtain a reconstructed image; Comparing the sizes of corresponding parts in the stacking direction of the multiple slice objects in the reconstructed image and the reference image to obtain a comparison result, where the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample; Based on the comparison result, evaluating the accuracy of the inter-slice splicing of the three-dimensional image reconstruction method for the multiple slice objects.

2. The evaluation method according to claim 1, characterized in that The comparing the sizes of corresponding parts in the stacking direction of the multiple slice objects in the reconstructed image and the reference image to obtain a comparison result includes: Determining cross-sectional images corresponding to each other in the reconstructed image and the reference image, where the tissue cross-sections included in the cross-sectional images are parallel to the stacking direction; In the mutually corresponding cross-sectional images, respectively determining the maximum sizes of the tissue cross-sections in the stacking direction; Comparing the maximum sizes of the mutually corresponding cross-sectional images to obtain the comparison result.

3. The evaluation method according to claim 1, characterized in that, There are multiple tissue samples, and the comparison result includes a result corresponding to each tissue sample. Among them, the evaluating the accuracy of the inter-slice splicing of the three-dimensional image reconstruction method for the multiple slice objects based on the comparison result includes: Determining the statistical value of all comparison results; Based on the statistical value and the number of slicing operations performed on each tissue sample, determining the accuracy of the inter-slice splicing of the three-dimensional image reconstruction method for the multiple slice objects.

4. The evaluation method according to claim 1, wherein The imaging data is obtained by the following method: Determining the number of slicing operations according to the size of the tissue sample; According to the number of slicing operations, slicing the tissue sample according to the number of slicing operations in the fixed field of view of the imaging device, and after each slicing operation, using the imaging device to perform at least one imaging on the current slice object obtained by the current slicing operation to obtain imaging data corresponding to each slicing operation.

5. The evaluation method according to claim 4, characterized in that, The using the imaging device to perform at least one imaging on the current slice object includes: Using the imaging device to perform at least one imaging on the current slice object until imaging data of all positions in the current slice object is obtained.

6. The evaluation method according to claim 4, wherein The using the imaging device to perform at least one imaging on the current slice object includes: According to the imaging field of view of the imaging device, a preset planar imaging redundancy overlap amount, and a preset imaging order, using the imaging device to perform multiple imagings on the current slice object, where the planar imaging redundancy overlap amount represents the overlap amount between the imaging regions of two adjacent imagings when performing multiple imagings on the same slice object. Among them, using the three-dimensional image reconstruction method to be evaluated to perform three-dimensional reconstruction on the multi-group imaging data to obtain a reconstructed image, including: Performing planar splicing on each group of imaging data to obtain a planar spliced image corresponding to each group of imaging data; Based on each planar spliced image, the slice thickness of the slice, the imaging depth of the imaging device, and the depth imaging redundant overlap amount, using the three-dimensional image reconstruction method to splice each planar spliced image in the stacking direction to obtain the reconstructed image.

7. An evaluation device for the inter-slice splicing accuracy of biological tissue imaging, characterized in that, The evaluation device includes: An acquisition unit configured to acquire multi-group imaging data obtained by an imaging device imaging a plurality of slice objects of a tissue sample, where the plurality of slice objects are a plurality of tissue slices obtained by performing multiple slicing operations on the tissue sample or a plurality of cross-sections obtained by performing multiple slicing operations on the tissue sample, the multi-group imaging data corresponds to the plurality of slice objects one by one, and each group of imaging data includes at least one imaging data of the corresponding slice object; A reconstruction unit configured to use the three-dimensional image reconstruction method to be evaluated to perform three-dimensional image reconstruction on the multi-group imaging data to obtain a reconstructed image; A comparison unit configured to compare the sizes of corresponding parts of the reconstructed image and the reference image in the stacking direction of the plurality of slice objects to obtain a comparison result, where the reference image is obtained by performing non-destructive three-dimensional imaging on the tissue sample; An evaluation unit configured to evaluate the accuracy of inter-slice splicing of the three-dimensional image reconstruction method for the plurality of slice objects based on the comparison result.

8. An evaluation system for the splicing accuracy between slices in biological tissue imaging, characterized in that, The evaluation system includes a mobile stage, a slicer, and a computing device, The computing device receives imaging data obtained by an imaging device imaging a tissue sample placed on the mobile stage, and the slicer is used to perform slicing operations on the tissue sample, The computing device includes a processor and a memory for storing instructions executable by the processor, where when the instructions executable by the processor are run by the processor, the processor is caused to execute the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the computing device, the computing device is enabled to execute the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to any one of claims 1 to 6.

10. A computer program product, comprising computer-executable instructions, characterized in that, The computer-executable instructions, when executed by at least one processor, implement the method for evaluating the inter-slice splicing accuracy of biological tissue imaging according to any one of claims 1 to 6.

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