Methods, apparatuses, electronic devices, and media for bone microstructure analysis around a bone prosthesis

Abstract

The present disclosure relates to the technical field of bone prosthesis implanted into human body, and particularly relates to a bone microstructure analysis method and device around bone prosthesis, an electronic device and a medium. The bone microstructure analysis method around bone prosthesis comprises the following steps: acquiring a perspective image of a human body region where a bone prosthesis is located; segmenting a bone image around the bone prosthesis from the perspective image; converting the bone image around the bone prosthesis into a preset rectangular bone quality image; and dividing the preset rectangular bone quality image into a plurality of rectangular block images, wherein the rectangular block images are used for quantitative analysis of the bone microstructure around the bone prosthesis.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of bone prosthesis implanted into human body, and particularly relates to a method and device for analyzing bone microstructure around bone prosthesis, an electronic device and a medium. BACKGROUND

[0002] Bone prosthesis is also called repair body in medicine, which is a medical device used to replace a certain limb, organ or tissue of human body. Bone prosthesis replacement has a wide application in the treatment of orthopedic diseases. For example, joint replacement (TJA) is one of the most effective methods for treating advanced joint diseases. Aseptic loosening of bone prosthesis is the most common long-term complication after bone prosthesis replacement, which is the bottleneck of limiting the service life of bone prosthesis. Recent studies have shown that early intervention (such as application of bisphosphonate) on bone prosthesis with loosening risk in the early postoperative period can reduce the 10-year loosening risk rate from 5.3% to 2.8%, i.e. about 47%. Therefore, early prediction of bone prosthesis stability is crucial to prolong the service life of bone prosthesis.

[0003] Early displacement of bone prosthesis (within 1-2 years after operation) measured by radiostereometric analysis (RSA) is highly correlated with long-term aseptic loosening rate, and is considered to be the most effective indicator for early evaluation of bone prosthesis stability. However, due to its special requirements for radiographic equipment and invasive tantalum bead implantation, it cannot be widely applied to all joint replacement patients.

[0004] Bone quality around bone prosthesis is another key factor affecting the stability of bone prosthesis. A large number of clinical data have confirmed that bone quality around bone prosthesis is significantly related to bone prosthesis displacement and long-term loosening rate. Bone mineral density (BMD, i.e. the content of bone mineral composition) and bone microstructure are two main factors determining bone quality. Both the decrease of bone mineral density and the destruction of bone microstructure will increase the brittleness of bone and make the bone worse in bearing pressure.

[0005] Currently, periprosthetic / systemic bone mineral density (BMD) has been found to be associated with long-term aseptic loosening; periprosthetic / systemic BMD is also associated with early prosthesis displacement. Therefore, periprosthetic bone BMD has become another important indicator for assessing prosthesis stability. Dual-energy X-ray absorptiometry (DXA) is the gold standard for assessing periprosthetic bone BMD. Periprosthetic bone is usually divided into 7 regions according to the Gruen partition for BMD assessment, but this method still has some limitations. First, bone quality is mainly determined by bone mineral content and bone microstructure, but BMD only represents bone mineral content and cannot measure bone microstructure. Second, according to the latest research, the bone adjacent to the prosthesis surface with a thickness of less than 1 mm is the key factor affecting prosthesis stability, but the Gruen partition of periprosthetic bone has large area, a small total number of regions, and includes both cortical and spongy bone—the overall "resolution" is poor and insufficient to capture changes in bone within small regions. Even if key local areas are redrawn, individual differences in the morphology of the bone around the prosthesis make it difficult to match and compare key areas between different patients, making it difficult to predict the stability of the bone prosthesis based on the quality of the bone around the prosthesis. Summary of the Invention

[0006] To address the problems in the related technologies, this disclosure provides a method, apparatus, electronic device, and medium for analyzing the bone microstructure around a bone prosthesis.

[0007] In a first aspect, this disclosure provides a method for analyzing the bone microstructure around a bone prosthesis, including:

[0008] Obtain a perspective image of the human body region where the bone prosthesis is located;

[0009] The bone image surrounding the bone prosthesis is segmented from the perspective image;

[0010] The bone image surrounding the bone prosthesis is converted into a pre-defined rectangular bone image;

[0011] The bone image of the preset rectangle is divided into multiple rectangular block images, which are used for quantitative analysis of the bone microstructure around the bone prosthesis.

[0012] According to embodiments of this disclosure, converting the bone image surrounding the bone prosthesis into a preset rectangular bone image includes:

[0013] The bone image surrounding the bone prosthesis is converted into a predefined rectangular bone image using any of the following algorithms: segmented affine algorithm, raster-grid-based image deformation conversion algorithm, linear image deformation conversion algorithm, and nonlinear image deformation conversion algorithm.

[0014] According to an embodiment of the present disclosure, the perspective image is a perspective image containing the bone prosthesis, and the step of segmenting the bone prosthesis surrounding bone image from the perspective image comprises:

[0015] segmenting the bone prosthesis and surrounding bone part from the perspective image by a graph segmentation algorithm;

[0016] segmenting the bone prosthesis surrounding bone image from the bone prosthesis and surrounding bone part by removing the bone prosthesis part from the bone prosthesis and surrounding bone part by the graph segmentation algorithm.

[0017] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image into a preset rectangular bone image comprises:

[0018] converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image; or

[0019] dividing the bone prosthesis surrounding bone image into multiple regions, and converting the multiple regions into corresponding regions of the preset rectangular bone image respectively, so as to obtain the preset rectangular bone image.

[0020] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image comprises using any one of the following algorithms to convert the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image: piecewise affine algorithm, point lattice grid-based image deformation conversion algorithm, linear-based image deformation conversion algorithm, and non-linear-based image deformation conversion algorithm.

[0021] The step of converting the multiple regions into corresponding regions of the preset rectangular bone image respectively comprises using any one of the following algorithms to convert the multiple regions into corresponding regions of the preset rectangular bone image respectively: piecewise affine algorithm, point lattice grid-based image deformation conversion algorithm, linear-based image deformation conversion algorithm, and non-linear-based image deformation conversion algorithm.

[0022] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image by using the piecewise affine algorithm comprises:

[0023] covering a first dot lattice uniformly to the preset rectangle;

[0024] scaling a second dot lattice with the same distribution as the first dot lattice according to the shape of the bone prosthesis surrounding bone, and covering the second dot lattice onto the bone prosthesis surrounding bone image;

[0025] establishing a piecewise affine conversion model based on the first dot lattice and the second dot lattice;

[0026] Convert the bone-prosthesis-surrounding-bone image into a preset-rectangular bone quality image through the piecewise affine conversion model.

[0027] According to an embodiment of the present disclosure, the converting the multiple regions into corresponding regions of the preset-rectangular bone quality image respectively using the piecewise affine algorithm comprises:

[0028] Uniformly cover multiple first dot arrays into the corresponding regions respectively;

[0029] Scale and cover multiple second dot arrays respectively distributed in the same way as the multiple first dot arrays onto the multiple regions according to the shape of the bone-prosthesis-surrounding-bone;

[0030] Establish a piecewise affine conversion model based on the first dot arrays and the corresponding second dot arrays;

[0031] Convert the multiple regions into corresponding regions of the preset-rectangular bone quality image through the piecewise affine conversion model.

[0032] According to an embodiment of the present disclosure, the dividing the preset-rectangular bone quality image into multiple rectangular block images comprises:

[0033] Uniformly divide the preset-rectangular bone quality image into multiple rectangular block images.

[0034] According to an embodiment of the present disclosure, the method further comprises: using the rectangular block images to quantitatively analyze the bone microstructure surrounding the bone prosthesis.

[0035] According to an embodiment of the present disclosure, the using the rectangular block images to quantitatively analyze the bone microstructure surrounding the bone prosthesis comprises:

[0036] Quantitatively analyze the bone microstructure of the preset-rectangular bone quality image as a whole;

[0037] Statistically analyze bone structure parameters in the rectangular block images, the bone structure parameters being the quantitative analysis results of the bone microstructure.

[0038] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure of the preset-rectangular bone quality image as a whole comprises:

[0039] Quantitatively analyze the bone microstructure of the preset-rectangular bone quality image as a whole using any one of the following ways: strut analysis, Haralick feature analysis based on a gray-level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0040] According to an embodiment of the present disclosure, the bone microstructure quantitative analysis of the preset rectangular bone image as a whole using the strut analysis comprises:

[0041] The preset rectangular bone image is up-sampled to obtain an up-sampled image;

[0042] The up-sampled image is subjected to density correction to remove image darkness caused by bone density;

[0043] The density-corrected image is converted into a binary image;

[0044] The binary image is converted into a topological strut image containing only topological struts of bone microstructure;

[0045] The parameters of each strut in the binary image and the topological strut image are subjected to quantitative analysis to obtain quantitative analysis results.

[0046] According to an embodiment of the present disclosure, the bone microstructure quantitative analysis of the bone prosthesis surrounding bone image as a whole using the strut analysis comprises:

[0047] The bone microstructure quantitative analysis of the bone prosthesis surrounding bone image as a whole is performed;

[0048] The corresponding part of the bone prosthesis surrounding bone image corresponding to each rectangular block image is determined;

[0049] The bone structure parameters in the rectangular block image are counted, which are the bone microstructure quantitative analysis results of the corresponding part of the bone prosthesis surrounding bone image corresponding to the rectangular block.

[0050] According to an embodiment of the present disclosure, the bone microstructure quantitative analysis of the bone prosthesis surrounding bone image as a whole comprises:

[0051] The bone microstructure quantitative analysis of the bone prosthesis surrounding bone image as a whole is performed using any of the following methods: strut analysis, Haralick feature analysis based on gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0052] According to an embodiment of the present disclosure, the bone microstructure quantitative analysis of the bone prosthesis surrounding bone image as a whole using the strut analysis comprises:

[0053] The bone prosthesis surrounding bone image is up-sampled to obtain an up-sampled image;

[0054] The up-sampled image is subjected to density correction to remove image darkness caused by bone density;

[0055] convert the density-corrected image into a binary image;

[0056] convert the binary image into a topological strut image containing only topological struts of bone microstructure;

[0057] quantitatively analyze parameters of each strut in the binary image and the topological strut image to obtain a quantitative analysis result.

[0058] According to an embodiment of the present disclosure, the method further comprises:

[0059] predicting the stability of the bone prosthesis based on bone structure parameters in the rectangular block image whose correlation with the displacement of the bone prosthesis meets a preset condition.

[0060] According to an embodiment of the present disclosure, the method further comprises:

[0061] predicting the stability of the bone prosthesis based on bone structure parameters in the rectangular block image located on the lower surface of the diaphysis.

[0062] In a second aspect, an embodiment of the present disclosure provides a bone microstructure analysis device around a bone prosthesis, comprising:

[0063] an acquisition module configured to acquire a perspective image of a human body region where a bone prosthesis is located;

[0064] a segmentation module configured to segment a bone image around the bone prosthesis from the perspective image;

[0065] a conversion module configured to convert the bone image around the bone prosthesis into a preset rectangular bone quality image;

[0066] a division module configured to divide the preset rectangular bone quality image into a plurality of rectangular block images, the rectangular block images being used for quantitative analysis of bone microstructure around the bone prosthesis.

[0067] According to an embodiment of the present disclosure, the conversion of the bone image around the bone prosthesis into the preset rectangular bone quality image comprises:

[0068] the conversion of the bone image around the bone prosthesis into the preset rectangular bone quality image using any one of the following algorithms: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0069] According to an embodiment of the present disclosure, the perspective image is a perspective image containing the bone prosthesis, and the segmentation of the bone image around the bone prosthesis from the perspective image comprises:

[0070] segmenting a bone prosthesis and surrounding bone portion from the perspective image by a graph segmentation algorithm;

[0071] segmenting a bone prosthesis surrounding bone image from the bone prosthesis and surrounding bone portion by removing the bone prosthesis portion from the bone prosthesis and surrounding bone portion by the graph segmentation algorithm.

[0072] According to an embodiment of the present disclosure, the converting the bone prosthesis surrounding bone image into a preset rectangular bone image comprises:

[0073] converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image; or

[0074] dividing the bone prosthesis surrounding bone image into multiple regions, and converting the multiple regions into corresponding regions of a preset rectangular bone image respectively, so as to obtain the preset rectangular bone image.

[0075] According to an embodiment of the present disclosure, the converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image comprises using any one of the following algorithms to convert the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0076] The converting the multiple regions into corresponding regions of a preset rectangular bone image respectively comprises using any one of the following algorithms to convert the multiple regions into corresponding regions of the preset rectangular bone image respectively: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0077] According to an embodiment of the present disclosure, the converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image using a piecewise affine algorithm comprises:

[0078] covering a first dot lattice uniformly to the preset rectangle;

[0079] scaling a second dot lattice with the same distribution as the first dot lattice according to the shape of the bone prosthesis surrounding bone, and covering the second dot lattice onto the bone prosthesis surrounding bone image;

[0080] establishing a piecewise affine conversion model based on the first dot lattice and the second dot lattice;

[0081] converting the bone prosthesis surrounding bone image into the preset rectangular bone image through the piecewise affine conversion model.

[0082] According to an embodiment of the present disclosure, the converting the plurality of regions into corresponding regions of the preset rectangular bone quality image respectively using the piecewise affine algorithm comprises:

[0083] Uniformly covering a plurality of first dot arrays into the corresponding regions respectively;

[0084] Scaling and covering a plurality of second dot arrays respectively distributed in the same way as the plurality of first dot arrays onto the plurality of regions according to the shape of the bone surrounding the bone prosthesis;

[0085] Establishing a piecewise affine conversion model based on the first dot array and the corresponding second dot array;

[0086] Converting the plurality of regions into corresponding regions of the preset rectangular bone quality image through the piecewise affine conversion model.

[0087] According to an embodiment of the present disclosure, the dividing the preset rectangular bone quality image into a plurality of rectangular block images comprises:

[0088] Uniformly dividing the preset rectangular bone quality image into a plurality of rectangular block images.

[0089] According to an embodiment of the present disclosure, the bone microstructure analysis device surrounding the bone prosthesis further comprises an analysis module configured to quantitatively analyze the bone microstructure surrounding the bone prosthesis using the rectangular block image.

[0090] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure surrounding the bone prosthesis using the rectangular block image comprises:

[0091] Quantitatively analyzing the bone microstructure of the preset rectangular bone quality image as a whole;

[0092] Statistically analyzing bone structure parameters in the rectangular block image, the bone structure parameters being quantitative analysis results of the bone microstructure.

[0093] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure of the preset rectangular bone quality image as a whole comprises:

[0094] Quantitatively analyzing the bone microstructure of the preset rectangular bone quality image as a whole using any one of the following ways: strut analysis, Haralick feature analysis based on a gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0095] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure of the preset rectangular bone quality image as a whole using the strut analysis comprises:

[0096] performing up-sampling on the preset rectangular bone quality image to obtain an up-sampled image;

[0097] performing density correction on the up-sampled image to remove image darkness caused by bone density;

[0098] converting the density-corrected image into a binary image;

[0099] converting the binary image into a topological strut image containing only topological struts of bone microstructure;

[0100] performing quantitative analysis on parameters of each strut in the binary image and the topological strut image to obtain quantitative analysis results.

[0101] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure around the bone prosthesis using the rectangular block image comprises:

[0102] performing quantitative analysis of bone microstructure on the bone image around the bone prosthesis as a whole;

[0103] determining a corresponding part of the bone image around the bone prosthesis corresponding to each rectangular block image;

[0104] counting bone structure parameters in the rectangular block image, the bone structure parameters being quantitative analysis results of bone microstructure of the corresponding part of the bone image around the bone prosthesis corresponding to the rectangular block.

[0105] According to an embodiment of the present disclosure, the quantitative analysis of bone microstructure on the bone image around the bone prosthesis as a whole comprises:

[0106] performing quantitative analysis of bone microstructure on the bone image around the bone prosthesis as a whole using any of the following methods: strut analysis, Haralick feature analysis based on gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0107] According to an embodiment of the present disclosure, the quantitative analysis of bone microstructure on the bone image around the bone prosthesis as a whole using strut analysis comprises:

[0108] performing up-sampling on the bone image around the bone prosthesis to obtain an up-sampled image;

[0109] performing density correction on the up-sampled image to remove image darkness caused by bone density;

[0110] converting the density-corrected image into a binary image;

[0111] converting the binary image into a topological strut image containing only topological struts of bone microstructure;

[0112] Quantitative analysis is performed on parameters of each pillar in the binarized image and the topological pillar image, to obtain a quantitative analysis result.

[0113] According to an embodiment of the present disclosure, the bone microstructure around the bone prosthesis analysis device further comprises:

[0114] The first prediction module is configured to predict the stability of the bone prosthesis based on the bone structure parameters in the rectangular block image that meets the preset condition in terms of the displacement of the bone prosthesis.

[0115] According to an embodiment of the present disclosure, the bone microstructure around the bone prosthesis analysis device further comprises:

[0116] The second prediction module is configured to predict the stability of the bone prosthesis based on the bone structure parameters in the rectangular block image located on the lower surface of the diaphysis.

[0117] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the method of any one of the first aspect.

[0118] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions, and the computer instructions are executed by a processor to implement the method of the first aspect.

[0119] In a fifth aspect, a computer program product is provided, and the computer program product includes computer instructions, and the computer instructions are executed by a processor to implement the method of the first aspect.

[0120] According to the technical scheme provided by the embodiments of the present disclosure, the bone quality around the bone prosthesis on the perspective image is first converted into a preset rectangular bone prosthesis bone image, and then is segmented into a plurality of small rectangular block images, each of which can be matched between different patients. On this basis, the bone microstructure of each rectangular block image can be quantified by the quantitative analysis technology of the bone microstructure. The present disclosure provides a new idea for early prediction of aseptic loosening of the bone prosthesis, and according to the prediction result, early intervention can be implemented on the bone prosthesis with loosening risk to reduce the possibility of occurrence of long-term aseptic loosening.

[0121] It should be noted that the bone microstructure analysis result obtained based on the technical solution of the present disclosure cannot be directly used to diagnose whether the bone prosthesis is loose. The bone microstructure analysis result has a correlation with the possibility of loosening of the bone prosthesis, but cannot be used as a diagnostic index of loosening of the bone prosthesis. The diagnostic index of loosening of the bone prosthesis is the pathological tissue examination result of the bone prosthesis and the bone, and therefore the technical solution of the present disclosure is not for diagnostic or therapeutic purposes.

[0122] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0123] Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of the non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings:

[0124] Figure 1 A flowchart of a bone microstructure analysis method around a bone prosthesis according to an embodiment of the present disclosure is shown.

[0125] Figure 2 A schematic diagram showing segmentation of a bone image around a bone prosthesis from a perspective image containing the hip joint bone prosthesis according to an embodiment of the present disclosure is shown.

[0126] Figure 3 A schematic diagram showing conversion of a bone image around a bone prosthesis into a preset rectangular bone quality image and division into a rectangular block image is shown.

[0127] Figure 4 A bone structure parameter statistical method in a rectangular block image according to an embodiment of the present disclosure is shown.

[0128] Figure 5 A bone structure parameter statistical method in a rectangular block image according to another embodiment of the present disclosure is shown.

[0129] Figure 6A And 6B The correlation between the CT density estimated by the strut parameter and the BMD is shown.

[0130] Figure 7A The main area of the strut parameter around the bone prosthesis in the preset rectangular bone quality image converted from the bone image around the bone prosthesis is shown.

[0131] Figure 7B The change trend of each strut parameter over time in the preset rectangular bone quality image converted from the bone image around the bone prosthesis is shown.

[0132] Figure 7C A region correlation heat map of the preset rectangular bone quality image according to an embodiment of the present disclosure is shown.

[0133] Figure 7D An information richest region in a preset rectangular bone quality image according to an embodiment of the disclosure is shown.

[0134] Figure 7E The prediction effect of a bone structure parameter based on the information richest region on the displacement of a bone prosthesis one year after surgery according to an embodiment of the disclosure is shown.

[0135] Figure 8 A structural block diagram of a bone microstructure analysis device around a bone prosthesis according to an embodiment of the disclosure is shown.

[0136] Figure 9 A structural block diagram of an electronic device according to an embodiment of the disclosure is shown.

[0137] Figure 10 A structural schematic diagram of a computer system suitable for implementing a method according to an embodiment of the disclosure is shown. DETAILED DESCRIPTION

[0138] Hereinafter, exemplary embodiments of the disclosure will be described in detail with reference to the accompanying drawings so as to be easily implemented by those skilled in the art. Also, parts irrelevant to the description of the exemplary embodiments are omitted in the drawings for the sake of clarity.

[0139] In the disclosure, it should be understood that terms such as "include" or "have" are intended to indicate that there are features, numbers, steps, actions, components, parts or combinations thereof disclosed in the specification, and do not exclude the possibility of adding one or more other features, numbers, steps, actions, components, parts or combinations thereof.

[0140] It is additionally noted that the embodiments in the disclosure and the features in the embodiments can be combined with each other without conflict. The disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0141] In the disclosure, if an operation related to acquisition of user information or user data or an operation of showing user information or user data to others, the operation is an operation authorized, confirmed by the user, or actively selected by the user.

[0142] As mentioned above, early prediction of the stability of the bone prosthesis is crucial for prolonging the service life of the bone prosthesis, and the bone quality around the bone prosthesis is one of the key factors affecting the stability of the bone prosthesis. BMD and bone microstructure are two main factors determining bone quality. Both BMD decline and bone microstructure destruction will result in increased bone fragility and poor pressure bearing capacity. Bone microstructure is a network structure formed by a large number of interwoven trabeculae. The network distribution direction of the trabeculae is consistent with the direction of the pressure and tension borne by the bone, and the advantage or disadvantage of the structural distribution determines the degree to which the local bone quality can bear external forces such as support, stretching, and impact. However, the current evaluation method is limited to the detection of BMD, without considering the detection and analysis of bone microstructure, so it is impossible to comprehensively predict the stability of the bone prosthesis according to the bone quality around the bone prosthesis.

[0143] According to the latest research, the bone quality with a thickness of less than 1 millimeter adjacent to the surface of the bone prosthesis is a key factor affecting the stability of the bone prosthesis. However, the Gruen partition of the bone quality around the bone prosthesis has large area of each block, small number of total blocks, and contains both cortical bone and cancellous bone at the same time, which has poor overall "resolution" and is not sufficient to capture the changes in the bone quality in small blocks. Even if the local key blocks are redrawn, due to the individual differences in the morphology of the bone quality around the bone prosthesis, it is difficult to match and compare the key blocks between different patients.

[0144] To further analyze the microstructure of the bone quality around the bone prosthesis, better capture the changes in the bone quality in the key small blocks, and realize the matching of the small blocks between different patients, the present disclosure proposes a quantitative analysis method based on partition and bone microstructure. According to an embodiment of the present disclosure, the bone quality around the bone prosthesis on the perspective image is first converted into a preset rectangle, and then divided into a plurality of small rectangular blocks, each of which can be matched between different patients. The bone microstructure of each rectangular block is quantified by a quantitative analysis technique of bone microstructure. The present disclosure provides a new idea for early prediction of aseptic loosening of the bone prosthesis. According to the prediction result, early intervention can be implemented on the bone prosthesis with loosening risk to reduce the possibility of occurrence of aseptic loosening in the long term.

[0145] Figure 1 A flowchart of a bone microstructure analysis method around a bone prosthesis according to an embodiment of the present disclosure is shown. As shown in Figure 1 The bone microstructure analysis method around the bone prosthesis includes the following steps S101-S104:

[0146] In step S101, a perspective image of a human body region where the bone prosthesis is located is acquired;

[0147] In step S102, a bone image around the bone prosthesis is segmented from the perspective image;

[0148] In step S103, the bone-prosthesis-surrounding-bone image is converted into a preset rectangular bone quality image.

[0149] In step S104, the preset rectangular bone quality image is divided into a plurality of rectangular block images, and the rectangular block images are used for quantitative analysis of the bone microstructure surrounding the bone prosthesis.

[0150] According to an embodiment of the present disclosure, the perspective image can be an X-ray film.

[0151] According to an embodiment of the present disclosure, the perspective image is a perspective image containing the bone prosthesis, and the step of segmenting the bone-prosthesis-surrounding-bone image from the perspective image includes: segmenting the bone prosthesis and the surrounding bone quality part from the perspective image by a graph segmentation algorithm; and removing the bone prosthesis part from the bone prosthesis and the surrounding bone quality part by the graph segmentation algorithm to segment the bone-prosthesis-surrounding-bone image.

[0152] For example, the bone prosthesis can be a hip joint bone prosthesis (for example, the hip joint bone prosthesis can be a total hip joint bone prosthesis or a half hip joint bone prosthesis), the perspective image can be a perspective image containing the hip joint bone prosthesis, for example, a femur middle upper end orthogram, a pelvis orthogram, or the like. The step of segmenting the bone-prosthesis-surrounding-bone image from the perspective image includes: segmenting the hip joint bone prosthesis and the surrounding bone quality part (i.e., the femur part) from the perspective image by a graph segmentation algorithm; and removing the hip joint bone prosthesis part from the bone prosthesis and the surrounding bone quality part by the graph segmentation algorithm to segment the bone-prosthesis-surrounding-bone image.

[0153] Figure 2 A schematic diagram of segmenting the bone-prosthesis-surrounding-bone image from the perspective image containing the bone prosthesis according to an embodiment of the present disclosure is shown.

[0154] According to an embodiment of the present disclosure, the BoneFinder algorithm can be used to outline the contour of the bone prosthesis and the surrounding bone quality part from the perspective image containing the bone prosthesis to segment the bone prosthesis and the surrounding bone quality part. Alternatively, other image segmentation algorithms based on artificial intelligence, image segmentation algorithms based on image gray values or image contours, or the like can also be used to segment the bone prosthesis and the surrounding bone quality part from the perspective image containing the bone prosthesis.

[0155] Figure 2 The shown example is a perspective image containing a hip joint bone prosthesis, wherein the part surrounded by the white frame in part (a) is the bone prosthesis and the surrounding bone quality part.

[0156] Then, a grayscale-based algorithm can be used to remove the bone prosthesis portion from the bone prosthesis and surrounding bone to segment the bone image around the prosthesis. Alternatively, other AI-based image segmentation algorithms, image contour-based image segmentation algorithms, etc., can be used to remove the bone prosthesis portion from the bone prosthesis and surrounding bone to segment the bone image around the prosthesis. Figure 2 Part (b) is the image after removing the hip joint prosthesis. Figure 2 Part (c) is the image of the bone surrounding the bone prosthesis.

[0157] Due to individual differences in the morphology of the bone surrounding the bone prosthesis, it is difficult to match and compare key areas between different patients. Therefore, Figure 2 The bone image around the prosthesis shown in section (c) is converted into a bone image of a preset rectangle, and the preset rectangular bone image is divided into multiple rectangular block images. According to embodiments of this disclosure, the preset rectangle has a preset size, and the rectangular block images also have a preset size, but their size is smaller than the preset rectangle. Converting the bone images around the prosthesis of different patients into bone images of the same size preset rectangle and dividing them into multiple smaller rectangular block images facilitates matching and comparison of bone images between different patients.

[0158] According to embodiments of this disclosure, converting the bone image around the bone prosthesis into a preset rectangular bone image includes: using any one of the following algorithms to convert the bone image around the bone prosthesis into a preset rectangular bone image: a segmented affine algorithm, a bit-grid-based image deformation conversion algorithm, a linear image deformation conversion algorithm, and a nonlinear image deformation conversion algorithm.

[0159] According to an embodiment of this disclosure, the fluoroscopic image is a fluoroscopic image containing the bone prosthesis, and the step of segmenting the bone image surrounding the bone prosthesis from the fluoroscopic image includes: segmenting the bone prosthesis and surrounding bone portion from the fluoroscopic image using a graphic segmentation algorithm; and removing the bone prosthesis portion from the bone prosthesis and surrounding bone portion using a graphic segmentation algorithm to segment the bone image surrounding the bone prosthesis.

[0160] According to embodiments of this disclosure, converting the bone image around the bone prosthesis into a preset rectangular bone image includes: converting the bone image around the bone prosthesis as a whole into a preset rectangular bone image; or dividing the bone image around the bone prosthesis into multiple regions, and converting the multiple regions into corresponding regions of the preset rectangular bone image, thereby obtaining the preset rectangular bone image.

[0161] According to an embodiment of the present disclosure, the converting the bone image around the bone prosthesis into the preset rectangular bone image as a whole comprises converting the bone image around the bone prosthesis into the preset rectangular bone image as a whole by using any one of the following algorithms: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0162] According to an embodiment of the present disclosure, the converting the bone image around the bone prosthesis into the preset rectangular bone image as a whole by using the piecewise affine algorithm comprises: uniformly covering a first point lattice to the preset rectangle; scaling and covering a second point lattice with the same distribution as the first point lattice to the bone image around the bone prosthesis according to the shape of the bone image around the bone prosthesis; establishing a piecewise affine conversion model based on the first point lattice and the second point lattice; and converting the bone image around the bone prosthesis into the preset rectangular bone image by using the piecewise affine conversion model.

[0163] According to an embodiment of the present disclosure, the converting the multiple regions into the corresponding regions of the preset rectangular bone image by using the piecewise affine algorithm comprises: uniformly covering multiple first point lattices to the corresponding regions, respectively; scaling and covering multiple second point lattices with the same distribution as the multiple first point lattices to the multiple regions according to the shape of the bone image around the bone prosthesis, respectively; establishing a piecewise affine conversion model based on the first point lattices and the corresponding second point lattices; and converting the multiple regions into the corresponding regions of the preset rectangular bone image by using the piecewise affine conversion model.

[0164] According to an embodiment of the present disclosure, the second point lattice with the same distribution as the first point lattice refers to that the number of rows and columns of the point of the second point lattice and the spacing between the points are the same as those of the first point lattice.

[0165] Figure 3 A schematic diagram of converting a bone image around a bone prosthesis into a preset rectangular bone image and dividing the bone image into rectangular block images is shown.

[0166] With the hip joint bone prosthesis as an example, according to the embodiments of the present disclosure, the converting the bone image around the bone prosthesis into the preset rectangular bone quality image comprises: dividing the bone image around the bone prosthesis into two regions (i.e., the medial bone quality image and the lateral bone quality image); and converting the medial bone quality image and the lateral bone quality image into the corresponding regions of the preset rectangular bone quality image, respectively, to obtain the preset rectangular bone quality image.

[0167] Specifically, as shown in part (a) of the present disclosure, the bone image around the bone prosthesis is divided into the medial bone quality image and the lateral bone quality image, and then converted into the corresponding regions of the preset rectangular bone quality image, respectively. Figure 3 Figure 2 Specifically, as shown in part (a) of the present disclosure, the bone image around the bone prosthesis is divided into the medial bone quality image and the lateral bone quality image, and then converted into the corresponding regions of the preset rectangular bone quality image, respectively.

[0168] According to the embodiments of the present disclosure, the preset rectangle includes two regions corresponding to the medial bone quality image and the lateral bone quality image, respectively, which are referred to as the medial portion and the lateral portion, respectively. For example, the preset rectangle can be set to have 100*1200 pixels for the lateral portion and 100*1000 pixels for the medial portion. The converting the medial bone quality image and the lateral bone quality image into the corresponding regions of the preset rectangular bone quality image, respectively, comprises: using any one of the following algorithms to convert the medial bone quality image and the lateral bone quality image into the medial portion and the lateral portion of the preset rectangular bone quality image, respectively: piecewise affine algorithm, image deformation conversion algorithm based on dot matrix grid, image deformation conversion algorithm based on linearity, image deformation conversion algorithm based on non-linearity.

[0169] As an example, the embodiments of the present disclosure use the piecewise affine algorithm as a detailed description, but those skilled in the art can understand that other algorithms such as the image deformation conversion algorithm based on dot matrix grid, the image deformation conversion algorithm based on linearity, and the image deformation conversion algorithm based on non-linearity can also achieve the same technical effects. In this embodiment, using the piecewise affine algorithm to convert the medial bone quality image and the lateral bone quality image into the medial portion and the lateral portion of the preset rectangular bone quality image, respectively, comprises: uniformly covering two first dot matrixes to the medial portion and the lateral portion, respectively; scaling two second dot matrixes with the same distribution as the two first dot matrixes according to the shape of the bone around the bone prosthesis and covering them onto the medial bone quality image and the lateral bone quality image, respectively; establishing a piecewise affine conversion model based on the first dot matrix and the second dot matrix; and converting the medial bone quality image and the lateral bone quality image into the preset rectangular bone quality image through the piecewise affine conversion model.

[0170] ​Two dense dot arrays are evenly covered to the medial portion and the lateral portion respectively, and the two dot arrays are referred to as first dot arrays. Two second dot arrays with the same distribution as the two first dot arrays are scaled and covered to the medial bone quality image and the lateral bone quality image according to the shape of the bone around the bone prosthesis, that is, the second dot array with the same distribution as the first dot array covering the medial portion is scaled and covered to the medial bone quality image, and the second dot array with the same distribution as the first dot array covering the lateral portion is scaled and covered to the lateral bone quality image.

[0171] A segmented affine conversion model is established based on the first dot array and the second dot array, and the bone image around the bone prosthesis is converted into a preset rectangular bone quality image through the segmented affine conversion model, as shown in part (b) of FIG. 1. Figure 3

[0172] According to an embodiment of the present disclosure, the preset rectangular bone quality image is divided into a plurality of rectangular block images, including evenly dividing the preset rectangular bone quality image into a plurality of rectangular block images.

[0173] For example, as shown in part (c) of FIG. 1, the preset rectangular bone quality image of part (b) is evenly divided into a plurality of rectangular block images, in which the lateral portion is divided into 10*24 rectangular block images, and the medial portion is divided into 10*20 rectangular block images. Each rectangular block image can have the same size. Figure 3 Figure 3 In this way, the bone image around the bone prosthesis of different patients can be converted into rectangular block images with the same size, so as to facilitate the matching and comparison of key block images between different patients.

[0174] According to an embodiment of the present disclosure, after obtaining the rectangular block image, the bone structure parameters in the rectangular block image are counted, so as to realize the quantitative analysis of the bone microstructure around the bone prosthesis.

[0175] According to an embodiment of the present disclosure, after obtaining the rectangular block image, the bone structure parameters in the rectangular block image are counted, so as to realize the quantitative analysis of the bone microstructure around the bone prosthesis.

[0176] The present disclosure provides two methods for counting bone structure parameters in rectangular block images.

[0177] Figure 4 A method for counting bone structure parameters in rectangular block images according to an embodiment of the present disclosure is shown.

[0178] Specifically, in step S401, the quantitative analysis of the bone microstructure is performed on the preset rectangular bone quality image as a whole, and in step S402, the bone structure parameters in the rectangular block image are counted, which are the quantitative analysis results of the bone microstructure.

[0179] ​​According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the preset rectangular bone image as a whole comprises: upsampling the preset rectangular bone image to obtain an upsampled image; performing density correction on the upsampled image to remove image darkness caused by bone density; converting the density-corrected image into a binary image; converting the binary image into a topological strut image containing only topological struts of bone microstructure; and performing quantitative analysis on parameters of each strut in the binary image and the topological strut image to obtain a quantitative analysis result.

[0180] Specifically, the upsampling ratio of the preset rectangular bone image can be 400%, or other upsampling ratios can also be used. As an example, the upsampling can be implemented using quadratic interpolation, but those skilled in the art can understand that other upsampling methods can also be used, such as linear interpolation-based upsampling method, deep learning-based upsampling method, Unpooling-based upsampling method, etc.

[0181] According to an embodiment of the present disclosure, the smoothing of the image data is implemented by Gaussian blur processing (sd=5) on the upsampled image to obtain a smoothed image, and the density correction is completed by subtracting the smoothed image from the upsampled image. In this embodiment, as an example, the smoothing of the image data is implemented by Gaussian blur processing, but those skilled in the art can understand that other smoothing algorithms can also be used, such as linear or nonlinear image data smoothing algorithm, or deep learning-based image data smoothing algorithm, to implement the smoothing of the image data.

[0182] According to an embodiment of the present disclosure, the density-corrected image can be converted into a binary image, for example, converted into a binary image according to the median.

[0183] According to an embodiment of the present disclosure, the binary image is converted into a topological strut image containing only topological struts of bone microstructure, also known as skeletonization conversion.

[0184] According to an embodiment of the present disclosure, the quantitative analysis of the parameters of each strut in the binary image and the topological strut image comprises determining at least one of the following parameters as the quantitative analysis result according to the binary image and the topological strut image:

[0185] • HDA: proportion of high-density area in the binary image

[0186] • TSL: total length of all struts per unit area

[0187] • nS: total number of struts per unit area

[0188] • Strut E-E: end-end strut ratio

[0189] • Strut J-E: junction-end strut ratio

[0190] • Strut J-J: junction-junction strut ratio

[0191] • Strut IC: independent ring strut ratio

[0192] Figure 5 A method for bone structure parameter statistics in a rectangular block image is shown according to another embodiment of the disclosure.

[0193] Specifically, in step S501, the bone microstructure of the bone image around the bone prosthesis as a whole is quantitatively analyzed. In step S502, the corresponding part of the bone image around the bone prosthesis corresponding to each rectangular block is determined. In step S503, the bone structure parameters in the rectangular block image are counted, which are the quantitative analysis results of the bone microstructure of the corresponding part of the bone image around the bone prosthesis corresponding to the rectangular block.

[0194] According to an embodiment of the disclosure, the quantitative analysis of the bone microstructure of the bone image around the bone prosthesis as a whole includes: upsampling the bone image around the bone prosthesis to obtain an upsampled image; density correction is performed on the upsampled image to remove image darkness caused by bone density; the density-corrected image is converted into a binary image; the binary image is converted into a topological strut image containing only topological struts of bone microstructure; and the parameters of each strut in the binary image and the topological strut image are quantitatively analyzed to obtain the quantitative analysis results.

[0195] Specifically, the up-sampling ratio of the bone image around the bone prosthesis can be 400%, or other up-sampling ratios can be used. As an example, the up-sampling can be implemented using quadratic interpolation, but those skilled in the art can understand that other up-sampling methods can also be used, such as linear interpolation-based up-sampling method, deep learning-based up-sampling method, Unpooling-based up-sampling method, etc.

[0196] According to an embodiment of the disclosure, the smoothing of the image data is achieved by Gaussian blur processing (sd=5) on the upsampled image to obtain a smoothed image, and the density correction is completed by subtracting the smoothed image from the upsampled image. In this embodiment, as an example, the smoothing of the image data is achieved by Gaussian blur processing, but those skilled in the art can understand that other smoothing algorithms can also be used, such as linear or nonlinear image data smoothing algorithm, or deep learning-based image data smoothing algorithm, to achieve the smoothing of the image data.

[0197] According to embodiments of this disclosure, a density-corrected image can be converted into a binarized image, for example, by converting it into a binarized image based on the median.

[0198] According to embodiments of this disclosure, the binarized image is converted into a topological pillar image containing only bone microstructures, also known as skeletonization conversion.

[0199] According to embodiments of this disclosure, quantitative analysis is performed on the parameters of each pillar in the binarized image and the topological pillar image, including determining at least one of the following parameters as the quantitative analysis result based on the binarized image and the topological pillar image:

[0200] • HDA: The proportion of high-density regions in a binarized image

[0201] ●TSL: The sum of the lengths of all supports per unit area

[0202] ●nS: The total number of supports per unit area

[0203] ● Support EE: End-to-end support ratio

[0204] • Column JE: The ratio of section to end column

[0205] ● Support JJ: The ratio of section to section support

[0206] ●Column IC: Proportion of independent ring-shaped columns

[0207] Since the above quantitative analysis results are obtained from the bone images surrounding the prosthesis, in order to statistically analyze the bone structure parameters of each rectangular block image, it is necessary to determine the corresponding part of the bone image surrounding the prosthesis that corresponds to each rectangular block image. That is, for each rectangular block image, it is necessary to determine which part of the bone image surrounding the prosthesis it corresponds to, and then statistically analyze the bone structure parameters in that part of the bone image surrounding the prosthesis.

[0208] The above combination Figure 4 and Figure 5 This paper describes a quantitative analysis of bone microstructure based on a strut analysis algorithm. However, those skilled in the art will understand that texture analysis algorithms or feature extraction algorithms can also be used to achieve the quantitative analysis of bone microstructure. The feature extraction algorithm may include Haralick features based on the gray-level co-occurrence matrix, local binary patterns, threshold adjacency statistics, etc.

[0209] The present disclosure provides a method for quantitatively detecting the microstructure of bone surrounding a bone prosthesis. DXA measured bone mineral density (BMD) represents the local bone mineral content, while bone structure represents the distribution of bone mineral. Micro-CT (micro computed tomography) can accurately reconstruct the microstructure of bone trabeculae, but CT (computed tomography) based bone structure assessment around a bone prosthesis cannot be achieved due to metal artifact. The present disclosure provides a method for quantitatively detecting the microstructure of bone, which can clearly and intuitively parameterize the bone trabeculae structure in an X-ray film. The results of the present disclosure show that the bone structure parameters measured by the method are significantly correlated with the density values measured by CT, and the CT values estimated from the bone structure parameters are significantly correlated with the BMD values measured by DXA. Therefore, the bone structure parameters measured by the method of the present disclosure can be used as an additional supplement to the BMD measured by DXA, and can be used for the evaluation of the bone quality surrounding a bone prosthesis.

[0210] Figure 6A and Figure 6B The correlation between the CT density estimated from the strut parameters and the BMD is shown.

[0211] The CT density values have been shown to be linearly correlated with the BMD. To test the performance of the present disclosure, a CT density estimation model based on the bone structure parameters was first established using the X-ray film and CT examination of the proximal femur of 10 patients completed within the same time period (within one week). Subsequently, 28 patients who underwent hip X-ray film and DXA follow-up around the bone prosthesis were analyzed. The bone structure parameters around the bone prosthesis were used to estimate the CT density by the CT density estimation model, and the correlation analysis was performed with the BMD measured by DXA.

[0212] The analysis results show that the bone structure parameters of each zone in the hip X-ray film are significantly correlated with the average HU value of the corresponding region in the CT (multiple correlation coefficient = 0.69, P < 0.01). The CT density estimation model based on the bone structure parameters was established by linear regression, R2= 0.34, mse = 230.61. Subsequently, the CT density values were estimated from the bone structure parameters of each Gruen zone (see Figure 6A ). Among them, the estimated CT density of zones 3, 5, 6, and 7 has a significant correlation with the BMD measured by the gold standard DXA (Pearson's r = 0.36-0.56, P≤0.05), and although there is no statistical significance in zones 1 and 2, a trend of correlation is found. According to the BMD results measured by DXA, the bone samples of each Gruen zone are further divided into three types of samples with high, medium, and low bone mass. In Gruen zones 2, 3, 5, 6, and 7, the CT density estimated based on the bone structure parameters shows good discrimination performance for low bone mass samples (accuracy = 0.64%-0.86%; see Figure 6B ).

[0213] According to the embodiment of the present disclosure, the bone prosthesis surrounding bone image is converted into a preset rectangular bone image, and the preset rectangular bone image is divided into a plurality of rectangular block images, which significantly improves the resolution of detection and enables matching and comparison between different patients.

[0214] After the implantation of the bone prosthesis, the internal stress of the bone is significantly changed, and the bone remodeling of each part of the bone is changed universally. However, the pathological study of the bone prosthesis-bone interface shows that the stability of the bone prosthesis mainly depends on the bone adjacent to the bone prosthesis with a thickness of less than 1 mm. The area of the Gruen region is large, and the universal bone remodeling can be easily captured, but the small area change related to loosening can be missed. The embodiment of the present disclosure first converts the irregular bone prosthesis surrounding bone into a regular preset rectangle through a segmented affine algorithm, and then uniformly divides it into 10*44 small rectangular block images. The corresponding bone prosthesis surrounding bone size is about 1-2 mm (thickness) * 8 mm (length), which makes it possible for the rectangular block image to capture the small area change related to loosening. The implementation results of the present disclosure show that the preset rectangle unifies the shape of the bone prosthesis surrounding bone, and each rectangular block can be matched between different patients, which can better analyze the change trend of the bone structure in each rectangular block.

[0215] To verify the effect of the embodiment of the present disclosure, a total of 91 cases completed a one-year follow-up of the hip X-ray, and the bone prosthesis surrounding bone in the postoperative follow-up hip X-ray was sequentially divided into rectangular block images, quantitatively analyzed the bone microstructure, and obtained the bone structure parameters of each rectangular block image according to the above method. Match the rectangular block images of all patients, for each of the rectangular block images, analyze the correlation between the bone structure parameters of the rectangular block image and the follow-up time to determine the trend of the bone structure parameters in each rectangular block image over time.

[0216] Figure 7A The main area of the change of the bone prosthesis surrounding bone structure strut parameters over time in the preset rectangular bone image converted from the bone prosthesis surrounding bone image is shown, wherein the darker the color, the greater the degree of change of the strut parameters over time.

[0217] Figure 7B The change trend of each strut parameter over time in the preset rectangular bone image converted from the bone prosthesis surrounding bone image is shown, wherein the darker the color, the greater the degree of change of the strut parameter over time. Figure 7BIn the above formula, "HDA" represents the proportion of high-density areas in the binary image, "TSL" represents the total length of all struts in unit area, "nS" represents the total number of all struts in unit area, "E-E" represents the proportion of end-to-end struts in all struts, "J-E" represents the proportion of junction-to-end struts in all struts, "J-J" represents the proportion of junction-to-junction struts in all struts, and "IC" represents the proportion of independent ring struts in all struts.

[0218] The analysis results show that, after matching the bone around the prosthesis according to the rectangular block image, the bone around the prosthesis is mainly composed of J-J struts (junction-to-junction struts) and J-E struts (junction-to-end struts) in each rectangular block image, accounting for 57.93% and 36.87% of the total number of struts, respectively. The percentage of E-E struts (end-to-end struts) in the greater trochanter region (region 1) is 7.32±1.6%, which is significantly higher than that in other regions (average 4.32%), which may be related to the sparse bone structure in the greater trochanter region. During the one-year follow-up, the changes in the bone microstructure around the prosthesis mainly occurred in the femoral calcar, the medial femoral cortex, and the middle segment of the lateral femur. The overall HDA and nS in these regions significantly decreased over time (HDA: -0.25±0.25% / month, nS: -0.94±1.10 / region / month). The proportion of J-J struts in all struts significantly decreased over time (-0.61±0.98% / month), while the proportions of E-E struts and J-E struts in all struts increased over time (E-E struts: 0.19±0.48% / month; J-E struts: 0.46±0.95% / month), revealing the osteoporotic changes in the bone structure around the prosthesis during the follow-up period.

[0219] According to an embodiment of the present disclosure, converting the bone image around the prosthesis into a preset rectangular bone image and dividing the preset rectangular bone image into a plurality of rectangular block images can further identify the region with the most abundant information and better predict the micro-displacement of the prosthesis one year after the surgery.

[0220] According to an embodiment of the present disclosure, the bone structure parameters in the rectangular block image that meet the preset condition in terms of correlation with the displacement of the prosthesis are used to predict the stability of the prosthesis. For example, after obtaining the bone structure parameters in the rectangular block image, a correlation analysis can be further performed on the displacement of the prosthesis one year after the surgery, and the top 5% regions (22 regions) with high correlation are defined as the region with the most abundant information, or the rectangular block image with a correlation higher than a preset correlation threshold can be positioned as the region with the most abundant information. The bone structure parameters in the region with the most abundant information are used to predict the displacement of the prosthesis.

[0221] Figure 7CA region correlation heat map of the preset rectangular bone quality image is shown according to an embodiment of the present disclosure, wherein the darker the color of the rectangular block image, the higher the correlation with the prosthesis displacement one year after the operation.

[0222] Figure 7D A most information-rich region in the preset rectangular bone quality image is shown according to an embodiment of the present disclosure. As shown in Figure 7B , the most information-rich region related to the prosthesis displacement is located on the surface of the lower part of the diaphysis, which is consistent with the pathological research results of the prosthesis-bone interface. Therefore, the stability of the prosthesis can be predicted based on the bone structure parameters in the rectangular block image located on the surface of the lower part of the diaphysis.

[0223] Figure 7E The prediction effect of the bone structure parameters based on the most information-rich region on the prosthesis displacement one year after the operation is shown according to an embodiment of the present disclosure.

[0224] As shown in Figure 7E , the prediction effect of the bone structure parameters based on the most information-rich region on the prosthesis displacement one year after the operation is much better than the prediction effect based on the Gruen partition. This result confirms that the micro-displacement one year after the operation can be predicted by the method of the present disclosure, and the micro-displacement of the prosthesis one year after the operation is considered as an early manifestation of aseptic loosening. Therefore, the present disclosure can provide a new idea for early evaluation of aseptic loosening of the prosthesis, and subsequent early intervention can be directly performed on the prosthesis with loosening risk to reduce the possibility of long-term aseptic loosening.

[0225] Figure 8 A structural block diagram of a bone microstructure analysis device around a prosthesis is shown according to an embodiment of the present disclosure. Wherein the device can be realized by software, hardware or a combination of the two to become part or all of an electronic device.

[0226] As shown in Figure 8 , a bone microstructure analysis device 800 around a prosthesis includes an acquisition module 801, a segmentation module 802, a conversion module 803, and a division module 804.

[0227] The acquisition module 801 is configured to acquire a perspective image of a human body region where the prosthesis is located.

[0228] The segmentation module 802 is configured to segment a bone image around the prosthesis from the perspective image.

[0229] The conversion module 803 is configured to convert the bone image around the prosthesis into a preset rectangular bone quality image.

[0230] The dividing module 804 is configured to divide the preset rectangular bone image into a plurality of rectangular block images, and the rectangular block images are used for quantitative analysis of the bone microstructure around the bone prosthesis.

[0231] According to an embodiment of the present disclosure, the converting the bone image around the bone prosthesis into a preset rectangular bone image comprises:

[0232] The bone image around the bone prosthesis is converted into a preset rectangular bone image using any one of the following algorithms: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0233] According to an embodiment of the present disclosure, the perspective image is a perspective image containing the bone prosthesis, and the bone image around the bone prosthesis is segmented from the perspective image, comprising:

[0234] The bone prosthesis and the surrounding bone part are segmented from the perspective image by a graph segmentation algorithm;

[0235] The bone prosthesis part is removed from the bone prosthesis and the surrounding bone part by a graph segmentation algorithm, and the bone image around the bone prosthesis is segmented.

[0236] According to an embodiment of the present disclosure, the converting the bone image around the bone prosthesis into a preset rectangular bone image comprises:

[0237] The bone image around the bone prosthesis is converted into a preset rectangular bone image as a whole; or

[0238] The bone image around the bone prosthesis is divided into a plurality of regions, and the plurality of regions are respectively converted into corresponding regions of the preset rectangular bone image, so as to obtain the preset rectangular bone image.

[0239] According to an embodiment of the present disclosure, the converting the bone image around the bone prosthesis into a preset rectangular bone image as a whole comprises converting the bone image around the bone prosthesis into a preset rectangular bone image as a whole using any one of the following algorithms: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0240] The converting the plurality of regions into corresponding regions of the preset rectangular bone image comprises converting the plurality of regions into corresponding regions of the preset rectangular bone image using any one of the following algorithms: a piecewise affine algorithm, a point lattice grid-based image deformation conversion algorithm, a linear-based image deformation conversion algorithm, and a non-linear-based image deformation conversion algorithm.

[0241] According to an embodiment of the present disclosure, the converting the bone-prosthesis-surrounding bone image into a preset rectangular bone quality image as a whole using the piecewise affine algorithm comprises:

[0242] uniformly covering a first grid array to the preset rectangle;

[0243] scaling and covering a second grid array with the same distribution as the first grid array to the bone-prosthesis-surrounding bone image according to the shape of the bone-prosthesis-surrounding bone;

[0244] establishing a piecewise affine conversion model based on the first grid array and the second grid array;

[0245] converting the bone-prosthesis-surrounding bone image into the preset rectangular bone quality image through the piecewise affine conversion model.

[0246] According to an embodiment of the present disclosure, the converting the multiple regions into corresponding regions of the preset rectangular bone quality image respectively using the piecewise affine algorithm comprises:

[0247] uniformly covering a plurality of first grid arrays to the corresponding regions respectively;

[0248] scaling and covering a plurality of second grid arrays with the same distribution as the plurality of first grid arrays respectively to the multiple regions according to the shape of the bone-prosthesis-surrounding bone;

[0249] establishing a piecewise affine conversion model based on the first grid array and the corresponding second grid array;

[0250] converting the multiple regions into corresponding regions of the preset rectangular bone quality image through the piecewise affine conversion model.

[0251] According to an embodiment of the present disclosure, the dividing the preset rectangular bone quality image into a plurality of rectangular block images comprises:

[0252] uniformly dividing the preset rectangular bone quality image into a plurality of rectangular block images.

[0253] According to an embodiment of the present disclosure, the bone-prosthesis-surrounding bone microstructure analysis device further comprises an analysis module configured to quantitatively analyze the bone microstructure around the bone prosthesis using the rectangular block images.

[0254] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure around the bone prosthesis using the rectangular block images comprises:

[0255] quantitatively analyzing the bone microstructure as a whole for the preset rectangular bone quality image;

[0256] count the bone structure parameters in the rectangular block image, the bone structure parameters being quantitative analysis results of the bone microstructure.

[0257] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the preset rectangular bone image as a whole comprises:

[0258] upsampling the preset rectangular bone image to obtain an upsampled image;

[0259] performing density correction on the upsampled image to remove image darkness caused by bone density;

[0260] converting the density-corrected image into a binary image;

[0261] converting the binary image into a topological strut image containing only topological struts of the bone microstructure;

[0262] quantitatively analyzing parameters of each strut in the binary image and the topological strut image to obtain quantitative analysis results.

[0263] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the bone prosthesis surrounding bone image comprises:

[0264] performing quantitative analysis of the bone microstructure of the bone prosthesis surrounding bone image as a whole;

[0265] determining a corresponding part of the bone prosthesis surrounding bone image corresponding to each rectangular block image;

[0266] counting bone structure parameters in the rectangular block image, the bone structure parameters being quantitative analysis results of the bone microstructure of the corresponding part of the bone prosthesis surrounding bone image corresponding to the rectangular block.

[0267] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the bone prosthesis surrounding bone image as a whole comprises:

[0268] upsampling the bone prosthesis surrounding bone image to obtain an upsampled image;

[0269] performing density correction on the upsampled image to remove image darkness caused by bone density;

[0270] converting the density-corrected image into a binary image;

[0271] converting the binary image into a topological strut image containing only topological struts of the bone microstructure;

[0272] Quantitative analysis is performed on parameters of each pillar in the binarized image and the topological pillar image, to obtain a quantitative analysis result.

[0273] According to an embodiment of the present disclosure, the bone microstructure analysis device around the bone prosthesis further comprises:

[0274] The first prediction module is configured to predict the stability of the bone prosthesis based on bone structure parameters in the rectangular block image related to the displacement of the bone prosthesis satisfying a preset condition.

[0275] According to an embodiment of the present disclosure, the bone microstructure analysis device around the bone prosthesis further comprises:

[0276] The second prediction module is configured to predict the stability of the bone prosthesis based on bone structure parameters in the rectangular block image located on the lower surface of the diaphysis.

[0277] The present disclosure also discloses an electronic device, Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0278] As Figure 9 shown, the electronic device comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to an embodiment of the present disclosure.

[0279] The present disclosure provides a bone microstructure analysis method around a bone prosthesis, comprising:

[0280] Obtaining a perspective image of a human body region where the bone prosthesis is located;

[0281] Segmenting a bone image around the bone prosthesis from the perspective image;

[0282] Converting the bone image around the bone prosthesis into a preset rectangular bone quality image;

[0283] Dividing the preset rectangular bone quality image into a plurality of rectangular block images, the rectangular block images being used for quantitative analysis of the bone microstructure around the bone prosthesis.

[0284] According to an embodiment of the present disclosure, the conversion of the bone image around the bone prosthesis into a preset rectangular bone quality image comprises:

[0285] The bone image around the bone prosthesis is converted into a preset rectangular bone quality image using any one of the following algorithms: piecewise affine algorithm, point lattice grid-based image deformation conversion algorithm, linear-based image deformation conversion algorithm, and non-linear-based image deformation conversion algorithm.

[0286] According to an embodiment of the present disclosure, the fluoroscopy image is a fluoroscopy image containing the bone prosthesis, and the step of segmenting the bone prosthesis surrounding bone image from the fluoroscopy image comprises:

[0287] segmenting the bone prosthesis and surrounding bone part from the fluoroscopy image by a graph segmentation algorithm;

[0288] segmenting the bone prosthesis surrounding bone image from the bone prosthesis and surrounding bone part by removing the bone prosthesis part from the bone prosthesis and surrounding bone part by a graph segmentation algorithm.

[0289] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image into a preset rectangular bone image comprises:

[0290] converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image; or

[0291] dividing the bone prosthesis surrounding bone image into multiple regions, and converting the multiple regions into corresponding regions of the preset rectangular bone image respectively, so as to obtain the preset rectangular bone image.

[0292] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image comprises using any one of the following algorithms to convert the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image: piecewise affine algorithm, point lattice grid-based image deformation conversion algorithm, linear-based image deformation conversion algorithm, and non-linear-based image deformation conversion algorithm.

[0293] The step of converting the multiple regions into corresponding regions of the preset rectangular bone image respectively comprises using any one of the following algorithms to convert the multiple regions into corresponding regions of the preset rectangular bone image respectively: piecewise affine algorithm, point lattice grid-based image deformation conversion algorithm, linear-based image deformation conversion algorithm, and non-linear-based image deformation conversion algorithm.

[0294] According to an embodiment of the present disclosure, the step of converting the bone prosthesis surrounding bone image as a whole into a preset rectangular bone image using the piecewise affine algorithm comprises:

[0295] covering a first dot lattice uniformly to the preset rectangle;

[0296] scaling a second dot lattice with the same distribution as the first dot lattice according to the shape of the bone prosthesis surrounding bone, and covering the second dot lattice onto the bone prosthesis surrounding bone image;

[0297] establishing a piecewise affine conversion model based on the first dot lattice and the second dot lattice;

[0298] Convert the bone-prosthesis-surrounding-bone image into a preset-rectangular bone quality image through the piecewise affine conversion model.

[0299] According to an embodiment of the present disclosure, the converting the multiple regions into corresponding regions of the preset-rectangular bone quality image respectively using the piecewise affine algorithm comprises:

[0300] Uniformly covering multiple first dot arrays into the corresponding regions respectively;

[0301] Respectively scaling multiple second dot arrays with the same distribution as the multiple first dot arrays according to the shape of the bone-prosthesis-surrounding-bone and covering them onto the multiple regions respectively;

[0302] Establishing a piecewise affine conversion model based on the first dot arrays and the corresponding second dot arrays;

[0303] Converting the multiple regions into corresponding regions of the preset-rectangular bone quality image through the piecewise affine conversion model.

[0304] According to an embodiment of the present disclosure, the dividing the preset-rectangular bone quality image into multiple rectangular block images comprises:

[0305] Uniformly dividing the preset-rectangular bone quality image into multiple rectangular block images.

[0306] According to an embodiment of the present disclosure, the method further comprises: using the rectangular block images to quantitatively analyze the bone microstructure surrounding the bone prosthesis.

[0307] According to an embodiment of the present disclosure, the using the rectangular block images to quantitatively analyze the bone microstructure surrounding the bone prosthesis comprises:

[0308] Quantitatively analyzing the bone microstructure of the preset-rectangular bone quality image as a whole;

[0309] Statistically analyzing bone structure parameters in the rectangular block images, the bone structure parameters being the quantitative analysis results of the bone microstructure.

[0310] According to an embodiment of the present disclosure, the quantitatively analyzing the bone microstructure of the preset-rectangular bone quality image as a whole comprises:

[0311] Quantitatively analyzing the bone microstructure of the preset-rectangular bone quality image as a whole using any of the following ways: strut analysis, Haralick feature analysis based on gray-level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0312] According to an embodiment of the present disclosure, the bone microstructure quantitative analysis of the preset rectangular bone image as a whole using the strut analysis comprises:

[0313] The preset rectangular bone image is up-sampled to obtain an up-sampled image;

[0314] The up-sampled image is subjected to density correction to remove image darkness caused by bone density;

[0315] The density-corrected image is converted into a binary image;

[0316] The binary image is converted into a topological strut image containing only topological struts of bone microstructure;

[0317] The parameters of each strut in the binary image and the topological strut image are subjected to quantitative analysis to obtain quantitative analysis results.

[0318] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure around the bone prosthesis using the rectangular block image comprises:

[0319] The bone microstructure of the bone image around the bone prosthesis as a whole is subjected to quantitative analysis;

[0320] The corresponding part of the bone image around the bone prosthesis corresponding to each rectangular block image is determined;

[0321] The bone structure parameters in the rectangular block image are counted, which are the bone microstructure quantitative analysis results of the corresponding part of the bone image around the bone prosthesis corresponding to the rectangular block.

[0322] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the bone image around the bone prosthesis as a whole comprises:

[0323] The bone microstructure of the bone image around the bone prosthesis as a whole is subjected to quantitative analysis using any of the following methods: strut analysis, Haralick feature analysis based on gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

[0324] According to an embodiment of the present disclosure, the quantitative analysis of the bone microstructure of the bone image around the bone prosthesis as a whole using the strut analysis comprises:

[0325] The bone image around the bone prosthesis is up-sampled to obtain an up-sampled image;

[0326] The up-sampled image is subjected to density correction to remove image darkness caused by bone density;

[0327] convert the density-corrected image into a binary image;

[0328] convert the binary image into a topological strut image containing only topological struts of the bone microstructure;

[0329] quantitatively analyze parameters of each strut in the binary image and the topological strut image to obtain a quantitative analysis result.

[0330] According to an embodiment of the present disclosure, the method further includes:

[0331] predicting the stability of the bone prosthesis based on the bone structure parameters in the rectangular block image that meets the preset condition in terms of the displacement of the bone prosthesis.

[0332] According to an embodiment of the present disclosure, the method further includes:

[0333] predicting the stability of the bone prosthesis based on the bone structure parameters in the rectangular block image located on the lower surface of the diaphysis.

[0334] Figure 10 A structural diagram of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown.

[0335] As shown in Figure 10 , the computer system includes a processing unit that can execute various methods in the above embodiments according to programs stored in a read-only memory (ROM) or loaded from a storage section into a random access memory (RAM). Various programs and data required for the operation of the computer system are also stored in the RAM. The processing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0336] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN card, a modem, etc. The communication section performs communication processes via a network such as the Internet. A drive is also connected to the I / O interface as necessary. A removable medium, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is attached to the drive as necessary, so that a computer program read out therefrom is installed into the storage section as necessary. The processing unit can be implemented as a CPU, a GPU, a TPU, a FPGA, a NPU, etc.

[0337] In particular, the method described above can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for executing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication part, and / or installed from a removable medium.

[0338] The flow and block diagrams in the drawings show the architectural, functional and operational views of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code which comprises one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0339] The units or modules described in the embodiments of the present disclosure can be implemented by means of software, or by means of programmable hardware. The described units or modules can also be provided in a processor, and the names of these units or modules do not constitute a limitation on the units or modules themselves in some cases.

[0340] As another aspect, the present disclosure also provides a computer readable storage medium, which can be the computer readable storage medium contained in the electronic device or computer system in the above embodiments; or can exist separately, and is not assembled into the device. The computer readable storage medium stores one or more programs, which are used by one or more processors to execute the methods described in the present disclosure.

[0341] The above description is merely that of the preferred embodiments of the present disclosure and a description of the technical principles of the present disclosure. It should be understood by those skilled in the art that the inventive scope involved in the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by the combinations of the above technical features or equivalent features without departing from the inventive concept. For example, the technical solutions formed by the mutual replacement of the above features and the technical features with similar functions disclosed in the present disclosure (but not limited to) without departing from the inventive concept.

Claims

1. A method for analyzing bone microstructure around a bone prosthesis, comprising: obtaining a perspective image of a human body region where the bone prosthesis is located; segmenting a bone image around the bone prosthesis from the perspective image; converting the bone image around the bone prosthesis into a preset rectangular bone quality image; dividing the preset rectangular bone quality image into a plurality of rectangular block images, the rectangular block images being used for quantitative analysis of bone microstructure around the bone prosthesis, the quantitative analysis comprising: obtaining bone structure parameters in each of the rectangular block images by performing quantitative analysis of bone microstructure on the preset rectangular bone quality image or the bone image around the bone prosthesis as a whole; and respectively counting the bone structure parameters in each of the rectangular block images, wherein the bone microstructure is a mesh structure formed by interlaced bone trabeculae.

2. The method of claim 1, wherein, The conversion of the bone image around the bone prosthesis into the preset rectangular bone quality image comprises: using any of the following algorithms to convert the bone image around the bone prosthesis into the preset rectangular bone quality image: piecewise affine algorithm, image deformation conversion algorithm based on dot array grid, image deformation conversion algorithm based on linearity, image deformation conversion algorithm based on non-linearity.

3. The method of claim 1, wherein, The perspective image is a perspective image containing the bone prosthesis, and the segmentation of the bone image around the bone prosthesis from the perspective image comprises: segmenting the bone prosthesis and the surrounding bone quality part from the perspective image by a graph segmentation algorithm; removing the bone prosthesis part from the bone prosthesis and the surrounding bone quality part by a graph segmentation algorithm to segment the bone image around the bone prosthesis.

4. The method of claim 3, wherein, The conversion of the bone image around the bone prosthesis into the preset rectangular bone quality image comprises: converting the bone image around the bone prosthesis as a whole into the preset rectangular bone quality image; or dividing the bone image around the bone prosthesis into a plurality of regions, and converting each of the plurality of regions into a corresponding region of the preset rectangular bone quality image, thereby obtaining the preset rectangular bone quality image. 5.The method of claim 4, wherein: the conversion of the bone image around the bone prosthesis as a whole into the preset rectangular bone quality image comprises using any of the following algorithms to convert the bone image around the bone prosthesis as a whole into the preset rectangular bone quality image: piecewise affine algorithm, image deformation conversion algorithm based on dot array grid, image deformation conversion algorithm based on linearity, image deformation conversion algorithm based on non-linearity; the conversion of each of the plurality of regions into a corresponding region of the preset rectangular bone quality image comprises using any of the following algorithms to convert each of the plurality of regions into a corresponding region of the preset rectangular bone quality image: piecewise affine algorithm, image deformation conversion algorithm based on dot array grid, image deformation conversion algorithm based on linearity, image deformation conversion algorithm based on non-linearity.

6. The method of claim 5, wherein, The conversion of the bone image around the bone prosthesis as a whole into the preset rectangular bone quality image using the piecewise affine algorithm comprises: uniformly covering a first dot array to the preset rectangle; scaling a second dot array having the same distribution as the first dot array according to the shape of the bone image around the bone prosthesis, and covering the second dot array onto the bone image around the bone prosthesis; establish a segmented affine conversion model based on the first net point array and the second net point array; convert the bone image around the bone prosthesis into the bone quality image of the preset rectangle through the segmented affine conversion model.

7. The method of claim 5, wherein, The segmented affine algorithm is used to convert the multiple regions into corresponding regions of the bone quality image of the preset rectangle, including: a plurality of first net point arrays are uniformly covered on the corresponding regions; a plurality of second net point arrays with the same distribution as the plurality of first net point arrays are scaled and covered on the multiple regions according to the shape of the bone around the bone prosthesis; a segmented affine conversion model is established based on the first net point array and the corresponding second net point array; the multiple regions are converted into corresponding regions of the bone quality image of the preset rectangle through the segmented affine conversion model.

8. The method of claim 1, wherein, The bone quality image of the preset rectangle is divided into a plurality of rectangular block images, including: The bone quality image of the preset rectangle is uniformly divided into a plurality of rectangular block images.

9. The method of claim 1, further comprising: quantitatively analyzing the bone microstructure around the bone prosthesis using the rectangular block images.

10. The method of claim 9, wherein, The quantitative analysis of the bone microstructure around the bone prosthesis using the rectangular block images includes: quantitatively analyzing the bone microstructure of the bone quality image of the preset rectangle as a whole; statistically analyzing the bone structure parameters in the rectangular block images, which are quantitative analysis results of the bone microstructure.

11. The method of claim 10, wherein, The quantitative analysis of the bone microstructure of the bone quality image of the preset rectangle as a whole includes: quantitatively analyzing the bone microstructure of the bone quality image of the preset rectangle as a whole using any of the following methods: strut analysis, Haralick feature analysis based on gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

12. The method of claim 11, wherein, The quantitative analysis of the bone microstructure of the bone quality image of the preset rectangle as a whole using strut analysis includes: upsampling the bone quality image of the preset rectangle to obtain an upsampled image; correcting the density of the upsampled image to remove image darkness caused by bone density; converting the density-corrected image into a binary image; converting the binary image into a topological strut image containing only topological struts of the bone microstructure; quantitatively analyzing the parameters of each strut in the binary image and the topological strut image to obtain quantitative analysis results.

13. The method of claim 9, wherein, The quantitative analysis of the bone microstructure around the bone prosthesis using the rectangular block images includes: quantitatively analyzing the bone microstructure of the bone image around the bone prosthesis as a whole; determining the corresponding part of the bone image around the bone prosthesis corresponding to each rectangular block image; statistically analyzing the bone structure parameters in the rectangular block images, which are quantitative analysis results of the bone microstructure of the corresponding part of the bone image around the bone prosthesis corresponding to the rectangular block image.

14. The method of claim 13, wherein, The quantitative analysis of the bone microstructure of the bone image around the bone prosthesis as a whole includes: The bone microstructure of the bone prosthesis surrounding bone image as a whole is quantitatively analyzed by using any one of the following methods: strut analysis, Haralick feature analysis based on gray level co-occurrence matrix, local binary pattern analysis, and threshold adjacency algorithm.

15. The method of claim 14, wherein, The bone microstructure of the bone prosthesis surrounding bone image as a whole is quantitatively analyzed by using the strut analysis, and the method comprises the following steps: The bone prosthesis surrounding bone image is up-sampled to obtain an up-sampled image; The up-sampled image is density-corrected to remove the image darkness caused by bone density; The density-corrected image is converted into a binary image; The binary image is converted into a topological strut image containing only topological struts of bone microstructure; The parameters of each strut in the binary image and the topological strut image are quantitatively analyzed to obtain quantitative analysis results.

16. The method of claim 1, further comprising: predicting the stability of the bone prosthesis based on the bone structure parameters in the rectangular block images that meet the preset condition in terms of displacement of the bone prosthesis.

17. The method of claim 1, further comprising: predicting the stability of the bone prosthesis based on the bone structure parameters in the rectangular block images located on the lower surface of the diaphysis.

18. A device for analyzing bone microstructure surrounding a bone prosthesis, comprising: an acquisition module configured to acquire a perspective image of a human body region where a bone prosthesis is located; a segmentation module configured to segment a bone prosthesis surrounding bone image from the perspective image; a conversion module configured to convert the bone prosthesis surrounding bone image into a preset rectangular bone quality image; a division module configured to divide the preset rectangular bone quality image into a plurality of rectangular block images, the rectangular block images being used for quantitative analysis of bone microstructure surrounding the bone prosthesis, the quantitative analysis comprising: quantitatively analyzing bone microstructure by taking the preset rectangular bone quality image or the bone prosthesis surrounding bone image as a whole to obtain bone structure parameters in each rectangular block image of the plurality of rectangular block images; and respectively counting the bone structure parameters in each rectangular block image, wherein the bone microstructure is a mesh structure formed by interlaced bone trabeculae.

19. An electronic device, comprising: a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any one of claims 1-17.

20. A computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are executed by the processor to implement the method steps of any one of claims 1-17.

21. A computer program product comprising computer instructions, which, when executed by a processor, implement the method steps of any one of claims 1-17.

21. A computer program product comprising computer instructions, which, when executed by a processor, implement the method steps of any one of claims 1-17.

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