Bone mineral density detection method, device, equipment and medium

Abstract

The invention relates to a bone mineral density detection method, device and equipment and a medium. The method comprises the following steps: acquiring a to-be-processed computed tomography (CT) image of a target body part; determining a plurality of tissue CT values corresponding to the plurality of target body tissues in the target body part according to the pixel CT values in the to-be-processed CT image; wherein the target body tissues are other body tissues except the target skeleton in the target body part; calculating a density conversion relation corresponding to the CT image to be processed according to preset tissue densities and tissue CT values corresponding to the plurality of target body tissues; wherein the density conversion relation represents the relation between the pixel CT value and the density; and according to the density conversion relationship, determining voxel-level bone density distribution of the to-be-operated part in the target bone. Therefore, fine-grained bone mineral density detection in a three-dimensional space is achieved, and the data requirement of the granularity dimension of the bone mineral density during motion control of a mechanical arm and the like is well met.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing technology, and in particular to a bone density detection method, device, equipment and medium. Background Art

[0002] Bone mineral density (BMD) can reflect the density and strength of bones. In related technologies, a bone density corresponding to the entire object under test can be determined based on a computed tomography (CT) image, and the bone density can be used to reflect the overall bone condition of the object under test. However, the bone density determined by this method is an overall value, which cannot meet the need to determine the specific bone density corresponding to multiple positions in the bone when performing motion control such as a robotic arm. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a bone density detection method, device, equipment and medium.

[0004] In a first aspect, the present disclosure provides a method for detecting bone density, the method comprising:

[0005] Acquiring a computerized tomography (CT) image of a target body part to be processed;

[0006] Determine, according to the pixel CT values ​​in the CT image to be processed, a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part; wherein the target body tissues are other body tissues in the target body part except the target bones;

[0007] Calculating a density conversion relationship corresponding to the CT image to be processed according to the preset tissue densities corresponding to the multiple target body tissues and the tissue CT values; wherein the density conversion relationship represents the relationship between the pixel CT value and the density;

[0008] According to the density conversion relationship, the voxel-level bone density distribution of the part to be operated in the target bone is determined.

[0009] In a second aspect, the present disclosure provides a bone density detection device, the device comprising:

[0010] An acquisition module, used for acquiring a to-be-processed computed tomography (CT) image of a target body part;

[0011] A first determination module is used to determine a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part according to the pixel CT values ​​in the CT image to be processed; wherein the target body tissues are other body tissues in the target body part except the target bones;

[0012] A calculation module, configured to calculate a density conversion relationship corresponding to the CT image to be processed according to preset tissue densities corresponding to the plurality of target body tissues and the tissue CT values; wherein the density conversion relationship represents a relationship between the pixel CT value and the density;

[0013] The second determination module is used to determine the voxel-level bone density distribution of the part to be operated in the target bone according to the density conversion relationship.

[0014] In a third aspect, the present disclosure further provides an electronic device, the device comprising:

[0015] one or more processors;

[0016] a storage device for storing one or more programs,

[0017] When one or more programs are executed by one or more processors, the one or more processors implement the method provided in the first aspect.

[0018] In a fourth aspect, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which implements the method provided in the first aspect when the computer program is executed by a processor.

[0019] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0020] A bone density detection method, device, equipment and medium of an embodiment of the present disclosure include: obtaining a computer tomography CT image to be processed of a target body part; determining multiple tissue CT values ​​corresponding to multiple target body tissues in the target body part according to the pixel CT values ​​in the CT image to be processed; wherein the target body tissue is other body tissues other than the target bone in the target body part; calculating the density conversion relationship corresponding to the CT image to be processed according to the preset tissue density and tissue CT values ​​corresponding to the multiple target body tissues; wherein the density conversion relationship represents the relationship between the pixel CT value and the density; and determining the voxel-level bone density distribution of the part to be operated in the target bone according to the density conversion relationship. In the above scheme, the tissue CT values ​​corresponding to different body tissues are determined according to the pixel CT values ​​in the CT image to be processed, and the density conversion relationship between the pixel CT value and density of the CT image to be processed is determined according to the tissue CT value and the preset tissue density. The voxel-level bone density distribution corresponding to some target bones is determined according to the density conversion relationship, and fine-grained bone density detection in three-dimensional space is realized, which better meets the data requirements in the granularity dimension of bone density when performing motion control of robotic arms, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A schematic diagram of a bone density detection method provided in an embodiment of the present disclosure;

[0024] Figure 2 A schematic diagram of a process for determining a tissue CT value provided in an embodiment of the present disclosure;

[0025] Figure 3 A schematic diagram of a process for determining a target bone region provided by an embodiment of the present disclosure;

[0026] Figure 4 A schematic diagram of determining a three-dimensional bone region provided in an embodiment of the present disclosure;

[0027] Figure 5 A schematic diagram of a process for determining a target tissue region provided by an embodiment of the present disclosure;

[0028] Figure 6 A schematic diagram of a target mask provided by an embodiment of the present disclosure;

[0029] Figure 7 A schematic diagram of determining a density conversion relationship provided in an embodiment of the present disclosure;

[0030] Figure 8 A schematic diagram of determining bone density distribution provided in an embodiment of the present disclosure;

[0031] Fig. 9 A schematic diagram of another bone density detection method provided by an embodiment of the present disclosure;

[0032] Fig.10 A schematic diagram of a flow chart of another bone density detection method provided in an embodiment of the present disclosure;

[0033] Fig.11 A schematic diagram of the structure of a bone density detection device provided in an embodiment of the present disclosure;

[0034] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0037] Bone density can reflect the density and strength of bones. In the relevant technology, the bone density measurement method includes dual-energy X-ray absorptiometry, ultrasonic bone densitometry, quantitative bone density measurement (Quantitative Computed Tomography, QCT), etc. Dual-energy X-ray absorptiometry can more accurately measure the bone density of the spine, hip joint and other parts. Ultrasonic bone density measurement can evaluate bone density by measuring the propagation speed of ultrasound in bones. This ultrasonic bone density measurement method is usually used for the detection of small bones such as wrists and ankles. Quantum bone density measurement can measure the bone density of the spine and other parts through CT images.

[0038] Unicondylar Knee Arthroplasty (UKA) can be used to replace the surface of the medial or lateral chamber of the knee joint, replacing the damaged cartilage surface of the tibia and femur of the knee joint, retaining the anterior and posterior cruciate ligaments of the knee joint to achieve precise treatment. Before performing a unicompartmental knee replacement, it is necessary to determine the specific bone density of multiple locations in the bones in order to control the robotic arm. However, a bone density corresponding to the entire object being tested can be determined through a computed tomography image, and this bone density can only reflect the overall bone condition of the object being tested, and cannot meet the need to determine the specific bone density corresponding to multiple locations in the bone when performing motion control such as a robotic arm.

[0039] In order to solve at least one of the above technical problems, the bone density detection method provided by the embodiment of the present disclosure is described below. In the embodiment of the present disclosure, the bone density detection method can be performed by an electronic device. The electronic device can include a device with communication functions such as a tablet computer, a desktop computer, a laptop computer, etc., and can also include a device simulated by a virtual machine or a simulator.

[0040] Figure 1 FIG. 1 is a flow chart of a bone density detection method provided by an embodiment of the present disclosure. Figure 1 As shown, the bone density detection method may include the following steps.

[0041] Step 101, obtaining a computerized tomography (CT) image of a target body part to be processed.

[0042] The target body part may be a body part to be subjected to bone density detection. This embodiment does not limit the target body part. For example, the target body part may be a knee joint, an ankle joint, or the like. The CT image to be processed may be an image obtained by tomographic scanning of the target body part. The CT image to be processed may record body tissues such as bones, muscles, and fat.

[0043] In the disclosed embodiment, in order to construct a CT three-dimensional model, a CT machine is used to scan the target body part multiple times to obtain multiple CT images. The CT image to be processed can be the image closest to a preset position among the multiple CT images, and the preset position can be an abnormal position in the target body part. For example, if the target body part is a knee joint, the CT image can be the CT image located at the bottom layer among the multiple CT images. The bone density detection device obtains the CT image to be processed.

[0044] Step 102, determining a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in a target body part according to the pixel CT values ​​in the CT image to be processed; wherein the target body tissues are body tissues other than the target bones in the target body part.

[0045] Among them, the pixel CT value can be a numerical value used to measure the degree of X-ray absorption of the pixel at the corresponding position of the target body part in the CT image to be processed. The target body tissue can be a body tissue with known density recorded in the CT image to be processed. The density distribution of the target body tissue is relatively even, and thus can be recorded as a specific data. This embodiment does not limit the target body tissue. For example, the target body tissue may include fat, muscle, and the like. The tissue CT value can be used to measure the numerical value of the degree of X-ray absorption of a type of tissue as a whole in the image to be processed. Taking the target body part including fat and muscle as an example, the tissue CT value may include fat CT value and muscle CT value. The target bone may be a bone whose density distribution is to be determined. Taking the target body part as the knee joint as an example, the target bone may include femur and tibia.

[0046] In the embodiment of the present disclosure, after acquiring the CT image to be processed, the bone density detection device can read the pixel CT value in the CT image to be processed, and calculate the tissue CT value corresponding to the target body part based on the pixel CT value of the target body part in the position corresponding to the CT image to be processed.

[0047] Figure 2 A schematic diagram of a process for determining a tissue CT value provided by an embodiment of the present disclosure, such as Figure 2As shown, in some embodiments of the present disclosure, determining multiple tissue CT values ​​corresponding to multiple target body tissues in a target body part according to the pixel CT values ​​in the CT image to be processed includes:

[0048] Step 201, determining a target bone region corresponding to a target bone in a CT image to be processed, setting the CT value of pixels in the target bone region to 0, and obtaining an intermediate CT image.

[0049] The target bone region may be a region in the CT image to be processed for recording the target bone, and the target bone region may be a region extracted by a mask corresponding to the target bone. The intermediate CT image may be an image obtained by shielding a portion of the image recording the target bone in the CT image to be processed.

[0050] In this embodiment, the bone density detection device can perform image recognition segmentation on the CT image to be processed to obtain the target bone region, and set the pixel CT value of the pixel located in the target bone region in the CT image to be processed to 0 to obtain an intermediate CT image. Thus, by setting the pixel CT value of the target bone region to 0, the influence of the pixel CT value of the bone on the subsequent determination of the tissue CT value is avoided, and the accuracy of the tissue CT value is improved.

[0051] Figure 3 A flow chart of determining a target bone region provided in an embodiment of the present disclosure is shown in FIG3. In some embodiments of the present disclosure, determining a target bone region corresponding to a target bone in a CT image to be processed includes:

[0052] Step 301, perform model preprocessing and sliding window processing on the CT three-dimensional model to obtain multiple three-dimensional sub-models; wherein the model preprocessing includes voxel point distance unification processing and pixel CT value normalization processing; wherein the CT three-dimensional model is a model obtained by three-dimensional reconstruction based on the CT image to be processed.

[0053] The CT three-dimensional model may be a three-dimensional simulation model obtained by three-dimensionally reconstructing multiple CT images of the target body part. The model preprocessing may be a process performed before image recognition and segmentation of the CT three-dimensional model. The sliding window processing may be a segmentation process of the three-dimensional model through a preset sliding window. The pixel distance unification process may be a process of unifying the distances between pixels of the three-dimensional model. The pixel CT value normalization process may be a process of converting the pixel CT value into a range of 0 to 1.

[0054] Figure 4 A schematic diagram of determining a three-dimensional bone region provided by an embodiment of the present disclosure, such as Figure 4As shown, in this embodiment, the three-dimensional reconstruction software can perform three-dimensional reconstruction based on the CT image to be processed and other CT images obtained by scanning the target body part to obtain a CT three-dimensional model. The bone density detection device can read the CT three-dimensional model and process the CT three-dimensional model to obtain a three-dimensional sub-model. Specifically, the bone density detection device can unify the distance (Spacing) of voxels in the CT three-dimensional model to a preset distance through interpolation to obtain a first intermediate three-dimensional model. The preset distance can be set according to user needs, etc., and this embodiment is not limited. For example, the preset distance can be 0.8. Further, the CT values ​​corresponding to each voxel of the first intermediate three-dimensional model are normalized to a value within the range of 0 to 1 to obtain a second intermediate three-dimensional model. Further, through sliding window processing, the second intermediate three-dimensional model is divided into a plurality of regions of interest (ROI) of preset sizes, and each region of interest can be a three-dimensional sub-model. The preset size can be adjusted according to user needs, application scenarios, etc., and this embodiment is not limited. For example, the preset size can be 64*64*64.

[0055] Step 302, multiple three-dimensional sub-models are input into the trained bone segmentation model to obtain multiple feature sub-models of the target bone output by the bone segmentation model.

[0056] The bone segmentation model may be a deep learning model for extracting features from a three-dimensional model, and this embodiment does not limit the model structure of the bone segmentation model. The feature sub-model may be a three-dimensional model that characterizes the features of the target bone. For example, taking the target bone including the tibia and femur as an example, the feature sub-model may include the feature sub-models of the femur and tibia.

[0057] like Figure 4 As shown, in this embodiment, the bone density detection device can input the three-dimensional sub-model into the trained bone segmentation model to obtain the feature sub-model corresponding to each three-dimensional sub-model output by the bone segmentation model.

[0058] Step 303, determining a three-dimensional bone region corresponding to the target bone in the CT three-dimensional model according to the multiple feature sub-models, and performing dimension conversion processing on the three-dimensional bone region to obtain the target bone region.

[0059] The three-dimensional bone region may be a region representing the target bone in the CT three-dimensional model, and the three-dimensional bone region may be a three-dimensional region extracted by the three-dimensional mask of the tibia and femur in the CT three-dimensional model. Taking the target bone including the tibia and femur as an example, the three-dimensional bone region may be a region recording the tibia and femur in the CT three-dimensional model.

[0060] like Figure 4As shown, in this embodiment, the bone density detection device can perform data post-processing on the characteristic sub-model to determine the three-dimensional bone region in the CT three-dimensional model. And according to the three-dimensional to two-dimensional dimension conversion relationship between the CT three-dimensional model and the CT image to be processed, the three-dimensional bone region corresponding to the CT three-dimensional model is dimensionally converted to obtain the target bone region corresponding to the CT image to be processed.

[0061] The data post-processing may be a process of determining the three-dimensional region of the skeleton based on the feature sub-model, and this embodiment does not limit the specific process of the data post-processing. Figure 4 As shown, the data post-processing may include: merging the feature sub-models according to the positional relationship between the three-dimensional sub-models to obtain a first feature model with the same voxel interval as the first intermediate three-dimensional model; converting the first feature model into a second feature model with the same voxel interval as the CT three-dimensional model by interpolation; using the maximum value (argmax) operator to process the second feature model to obtain a three-dimensional mask corresponding to the target bone, and determining the three-dimensional region extracted by the three-dimensional mask as the bone three-dimensional region.

[0062] In the above scheme, the three-dimensional bone region corresponding to the target bone in the three-dimensional space is first determined, and then the three-dimensional bone region is converted from three-dimensional to two-dimensional to obtain the target bone region corresponding to the CT image to be processed in the two-dimensional space. Based on the three-dimensional bone region, the area corresponding to the target bone can be marked more accurately and intuitively, so that the user can more intuitively check whether there is any error in the determination of the three-dimensional bone region, and the accuracy of the target bone region is improved.

[0063] Step 202: determining a plurality of target tissue regions in the intermediate CT image based on a plurality of CT value ranges corresponding to a plurality of target body tissues.

[0064] The CT value range may be a preset range of pixel CT values ​​corresponding to the target body tissue, and the CT value range may be understood as an empirical value, and the present embodiment does not limit the CT value range. For example, the CT value range corresponding to muscle may be 20 to 80, and the CT value range corresponding to fat may be -70 to -130. The target tissue region may be a region in the intermediate CT image that characterizes the target body tissue.

[0065] In this embodiment, for each target body tissue, the bone density detection device can determine the target tissue area corresponding to each target body tissue in the intermediate CT image based on the pixel CT value within the corresponding CT value range in the intermediate CT image.

[0066] Figure 5 A schematic diagram of a process for determining a target tissue region provided by an embodiment of the present disclosure, such as Figure 5As shown, in some embodiments of the present disclosure, based on multiple CT value ranges corresponding to multiple target body tissues, multiple target tissue regions in the intermediate CT image are determined, including:

[0067] Step 501 : for each target body tissue, binarization processing is performed on the intermediate CT image according to the CT value range corresponding to the target body tissue to obtain an original mask of the target body tissue.

[0068] The original mask may be a mask of the target body tissue directly determined according to the CT value range.

[0069] In this embodiment, for each target body tissue, the bone density detection device can set the mask value corresponding to the pixel whose pixel CT value is within the CT value range corresponding to the target body tissue in the intermediate CT image to 1, and set the mask value corresponding to the pixel whose pixel CT value is not within the CT value range corresponding to the target body tissue to 0, to obtain the original mask.

[0070] Step 502: Process the original mask according to a preset region stitching algorithm to obtain a target mask, and determine the extraction region corresponding to the target mask as the target tissue region.

[0071] The region stitching algorithm can be used to eliminate the segmented parts in the interconnected regions in the original mask. The region stitching algorithm can be used to stitch the interconnected regions into a whole region. This embodiment does not limit the region stitching algorithm. The target mask can be a mask obtained by eliminating the segmented parts of the small regions in the original mask. The extracted region can be a region extracted by the mask.

[0072] In this embodiment, the bone density detection device can process the original mask with a Gaussian filter of a preset standard deviation (Sigma), binarize the mask after the Gaussian filter again according to a preset numerical threshold, and perform image corrosion on the mask after the binarization again to obtain a target mask. This embodiment does not limit the preset standard deviation. For example, the preset standard deviation can be 1. This embodiment does not limit the preset numerical threshold. For example, the preset numerical threshold can be 0.3. Further, the bone density detection device can determine the area extracted by the target mask as the target tissue area. Figure 6 A schematic diagram of a target mask provided by an embodiment of the present disclosure, such as Figure 6 As shown, the white area is the area extracted by the target mask, that is, the target tissue area.

[0073] Step 203: determining a plurality of tissue CT values ​​based on the pixel CT values ​​within the plurality of target tissue regions.

[0074] In this embodiment, for each target tissue region, the bone density detection device can calculate the pixel CT value of the pixels in the target tissue region according to a preset calculation formula to determine the tissue CT value corresponding to the target tissue region.

[0075] In some embodiments of the present disclosure, multiple tissue CT values ​​are determined based on pixel CT values ​​within multiple target tissue regions, including: for each target tissue region, determining an inscribed area of ​​a preset shape within the target tissue region, and calculating the tissue CT value based on the pixel CT values ​​within the inscribed area.

[0076] The preset shape may be a pre-set area shape, and this embodiment does not limit the preset area shape. For example, the preset shape may be a circle.

[0077] In this embodiment, for each target tissue region, the bone density detection device can determine the inscribed region of the preset shape in the target tissue region, and determine the average value of the pixel CT values ​​in the inscribed region as the tissue CT value of the corresponding target body tissue. Thus, by determining the inscribed region, the possible parts at the edge of the target tissue region that are not the target body tissue are filtered out, and at the same time, the larger area corresponding to the target body tissue for calculating the tissue CT value is determined, thereby improving the calculation accuracy of the tissue CT value.

[0078] Figure 7 A schematic diagram of determining a density conversion relationship provided by an embodiment of the present disclosure, such as Figure 7 As shown, after reading the CT image to be processed, the bone density detection device determines the muscle CT value and the fat CT value respectively. Specifically, the bone density detection device can set the pixel CT value corresponding to the target bone area in the CT image to be processed to 0 to obtain an intermediate CT image. For the muscle, the bone density detection device can set the mask value corresponding to the pixel whose pixel CT value is between 20 and 80 in the intermediate CT image to 1, and set the mask value corresponding to the pixel whose pixel CT value is not between 20 and 80 to 0, complete the binarization processing of the intermediate CT image based on the CT range of the muscle, and obtain the target muscle area. Further, the target muscle area is distance transformed to find the maximum inscribed circle of the target muscle area, and the area corresponding to the maximum inscribed circle is determined as the inscribed area corresponding to the muscle. The average value of the pixel CT values ​​in the inscribed area is calculated to obtain the muscle CT value.

[0079] For fat, the bone density detection device can set the mask value corresponding to the pixel with the pixel CT value between -70 and -130 in the intermediate CT image to 1, and set the mask value corresponding to the pixel with the pixel CT value not between -70 and -130 to 0, complete the binarization processing of the intermediate CT image based on the CT range of fat, and obtain the target fat area. Further, the distance transformation is performed on the target fat area to find the maximum inscribed circle of the target fat area, and the area corresponding to the maximum inscribed circle is determined as the inscribed area corresponding to the fat. The average value of the pixel CT value in the inscribed area is calculated to obtain the fat CT value.

[0080] Step 103, according to the preset tissue densities and tissue CT values ​​corresponding to the plurality of target body tissues, a density conversion relationship corresponding to the CT image to be processed is calculated; wherein the density conversion relationship represents the relationship between the pixel CT value and the density.

[0081] The preset tissue density may be a density corresponding to a preset target body tissue, and the preset density tissue may correspond to the target value tissue in a one-to-one manner. The density conversion relationship may be used to convert a pixel CT value into a density. This embodiment does not limit the relationship type of the density conversion relationship. For example, the density conversion relationship may be a linear relationship. The density conversion relationship is determined based on the CT image to be processed, and the density conversion relationship may be applicable to the CT image to be processed and the CT three-dimensional model.

[0082] In the embodiment of the present disclosure, the bone density determination device can perform fitting processing based on the target body tissue and its corresponding preset tissue density based on a preset relationship type to obtain a density conversion relationship.

[0083] For example, Figure 7 As shown, the preset relationship can be a linear relationship, and the preset relationship can be expressed as y=ax+b, where y represents density, x represents pixel CT value, a represents slope, and b represents intercept. The bone density detection device can perform a least squares fitting calculation on the preset relationship type according to the preset muscle density and muscle CT value of muscle and the preset fat density and fat CT value of fat to determine the density conversion relationship.

[0084] Step 104, determining the voxel-level bone density distribution of the part to be operated in the target bone according to the density conversion relationship.

[0085] Among them, the part to be operated can be the part of the target bone to be subjected to finishing operations such as grinding and cutting. The part to be operated can be the part of the target bone that overlaps with the bone prosthesis. The bone density distribution can be used to record multiple bone densities corresponding to multiple position points in the part to be operated. The position point can be all or part of the voxel points in the part to be operated in the CT three-dimensional model. The distribution of bone density at the voxel level can be characterized by the bone density distribution at the voxel level. Bone density can be used to characterize the mineral content of bones. It can be understood that if the part to be operated is a surface, the bone density can be distributed in a plane in a three-dimensional space, and if the part to be operated is an area, the bone density can be distributed in three dimensions.

[0086] In this embodiment, the bone density detection device can convert the CT values ​​of multiple position points located in the part to be operated in the CT three-dimensional model according to the density conversion relationship, obtain the bone density corresponding to each position point, and use the position point and its corresponding bone density as the bone density distribution. The CT value of the position point can be the voxel CT value of the position point, the voxel CT value can be the CT value corresponding to the voxel, and the voxel CT value and the pixel CT value can be the expression of the CT value corresponding to the smallest basic unit in different dimensional spaces.

[0087] For example, Figure 8 A schematic diagram of determining bone density distribution provided by an embodiment of the present disclosure is shown in FIG. Figure 8 As shown, in this embodiment, the bone density detection device can read the CT three-dimensional model through the planning software and calculate the part to be operated in the target bone. Specifically, the position and model of the bone prosthesis can be determined through the planning software, and the prosthesis three-dimensional model corresponding to the bone prosthesis is placed in the coordinate system where the CT three-dimensional model is located based on the prosthesis position, and the area where the bone three-dimensional area overlaps with the prosthesis three-dimensional model is determined as the part to be operated.

[0088] Furthermore, the bone density distribution of the part to be operated is calculated. Specifically, the bone density detection device can determine the voxel CT value corresponding to each position point in the part to be operated based on the CT three-dimensional model, substitute the voxel CT value into the density conversion relationship, and determine the bone density corresponding to the position point. The multiple position points and their corresponding bone densities are used as the bone density distribution of the part to be operated.

[0089] The bone density detection method of the embodiment of the present disclosure obtains a to-be-processed computed tomography CT image of a target body part; determines multiple tissue CT values ​​corresponding to multiple target body tissues in the target body part according to the pixel CT value in the to-be-processed CT image; wherein the target body tissue is other body tissues other than the target bone in the target body part; calculates the density conversion relationship corresponding to the to-be-processed CT image according to the preset tissue density and tissue CT value corresponding to the multiple target body tissues; wherein the density conversion relationship represents the relationship between the pixel CT value and the density; and determines the voxel-level bone density distribution of the to-be-operated part in the target bone according to the density conversion relationship. In the above scheme, the tissue CT values ​​corresponding to different body tissues are determined according to the pixel CT values ​​in the to-be-processed CT image, and the density conversion relationship between the pixel CT value and the density of the to-be-processed CT image is determined according to the tissue CT value and the preset tissue density, and the voxel-level bone density distribution corresponding to some target bones is determined according to the density conversion relationship, so as to realize fine-grained bone density detection in three-dimensional space, and better meet the data requirements of the granularity dimension of bone density when performing motion control of a robotic arm, etc.

[0090] In the related art, when operating hard bones with a robotic arm, the user needs to apply greater force to the robotic arm, which may cause fracture or exceed the torque limit of one or more axes of the robotic arm. Therefore, it is necessary to improve the safety of the robotic arm operating bones.

[0091] Fig. 9 A flow chart of another bone density detection method provided by the present disclosure is shown in FIG. Fig. 9 As shown, in some embodiments of the present disclosure, the bone density detection method further includes:

[0092] Step 901, each position point is determined as the current position point respectively, and among multiple bone density intervals, the bone density interval where the current bone density of the current position point is located is determined as the current interval; wherein the interval endpoint values ​​corresponding to the multiple bone density intervals are some of the values ​​selected from the multiple bone densities.

[0093] The current position point may be a position point currently being processed. A bone density interval may be a range of bone density. The current bone density may be the bone density corresponding to the current position point. The current interval may be a bone density interval including the current bone density. The interval endpoint value may be a critical point value defining the bone density interval.

[0094] In this embodiment, after determining the bone density corresponding to each position point, part of the bone density can be selected as the interval endpoint value, and the bone density interval can be established according to the interval endpoint values ​​adjacent to the numerical value. For each interval endpoint value, a simulation object composed of a preset material is used to simulate the bone with a bone density of the interval endpoint value, and the simulation object is operated by a robotic arm to determine the calibration control parameters that can prevent the speed and / or force data of the robotic arm from changing suddenly and meet the user's use requirements. The calibration control parameters correspond to the interval endpoint value. Furthermore, for each bone density interval, interpolation processing is performed according to the interval endpoint value of the bone density interval and its corresponding calibration control parameters to obtain a conversion interpolation function that corresponds to the bone density interval one by one.

[0095] Among them, the calibration control parameter can be a control parameter determined by calibration. The control parameter can be a parameter for controlling the movement of the robotic arm. Taking admittance control as an example, the control parameter can include a mass parameter and a damping parameter. The preset material can be a pre-set material for simulating bones of corresponding bone density. This embodiment does not limit the preset material. For example, the preset material can be a density board. The thickness of the simulation can be greater than a preset thickness threshold. The preset interpolation processing can be a processing based on a preset interpolation formula. This embodiment does not limit the preset interpolation formula. For example, the preset interpolation formula can be a Newton interpolation formula, etc. The conversion interpolation function can be used to convert bone density into corresponding control parameters.

[0096] In the above scheme, a sufficiently thick simulant is used to test and determine the calibration control parameters instead of using bones for simulation. This is because the bone density of bones varies greatly with depth. It is difficult to test and obtain better calibration control parameters at a smaller depth with the same bone density. This simulant can obtain more accurate calibration control parameters. Moreover, through interpolation processing, when the control parameters are subsequently determined, if the current bone density is consistent with the interval endpoint value, the calibration control parameters are used, and the better control parameters determined by the test are preferentially selected, thereby improving the control effect of the robotic arm.

[0097] In this embodiment, the bone density detection device may determine each position point as the current position point, and determine the bone density interval including the current bone density corresponding to the current position point as the current interval among multiple bone density intervals.

[0098] Step 902, converting the current bone density according to the conversion interpolation function corresponding to the current interval to obtain the current control parameters corresponding to the current position point; wherein the conversion interpolation function is determined by interpolating the interval endpoint values ​​of the current interval and the calibration control parameters corresponding to the interval endpoint values.

[0099] The calibration control parameters may be calibrated through experiments based on a simulant having a thickness greater than a preset thickness threshold.

[0100] Furthermore, the bone density detection device may input the current bone density into a conversion interpolation function corresponding to the current interval to obtain a current control parameter corresponding to the current bone density, and associate the current control parameter with the current position.

[0101] It can be understood that if the current bone density is consistent with the interval endpoint value, the current control parameter determined by the corresponding conversion interpolation function is the same as the calibrated control parameter obtained in the previous test. If the current bone density is inconsistent with the interval endpoint value, the new current control parameter is calculated by the corresponding conversion interpolation function.

[0102] Step 903: Control the robotic arm according to the current control parameters.

[0103] The robot arm may be a robot arm for operating the part of the target bone to be operated, and the robot arm may be a surgical robot. Taking the target body part as the knee joint as an example, the robot arm may be a unicompartmental surgical robot, and the unicompartmental surgical robot may be a robot for operating on a unilateral joint, and the unicompartmental surgical robot may be used to perform bone cutting and placement of bone prostheses. This embodiment does not impose any restrictions on the operating accuracy of the unicompartmental surgical robot, for example, the operating accuracy of the unicompartmental surgical robot may be sub-millimeter level.

[0104] In this embodiment, the bone density detection device can determine the control parameters for each position point recorded in the bone density distribution according to the bone density of the position point, and obtain multiple control parameters corresponding to multiple position points in the part to be operated. Then, according to the multiple control parameters, the robot arm is controlled in advance before the robot arm actually moves to the corresponding position point.

[0105] In the above scheme, the bone density distribution difference of the part to be operated on the target bone can be calculated before operating on the target bone, and the control parameters of the robotic arm can be optimized in advance based on the bone density distribution, thereby reducing the impact of bone density differences on the stability of the robotic arm, reducing the probability of robotic arm instability caused by hard bones, improving the stability, safety and reliability of the surgical robot, and improving the user experience.

[0106] Fig.10 A flow chart of another bone density detection method provided by the present disclosure is as follows: Fig.10 As shown, in some embodiments of the present disclosure, after controlling the mechanical arm according to the current control parameters, the bone density detection method further includes:

[0107] Step 1001, if the motion state of the robot arm at the current position point is an unstable state, the parameters of the robot arm are adjusted to obtain the corresponding corrected control parameters when the motion state at the current position point is a stable state.

[0108] Among them, the unstable state may be a situation where the robot arm cannot move according to the pre-planned state. The stable state may be a situation where the robot arm can move according to the pre-planned state. The corrected control parameter may be a control parameter when the robot arm can move according to the pre-planned state at the current position. This embodiment does not limit the determination of the corrected control parameter. For example, the corrected control parameter may be manually adjusted and set by the user, or the corrected control parameter may be set based on the parameter adjustment function of the robot arm itself.

[0109] In this embodiment, the detection system of the robot arm can periodically determine the high-frequency amplitude ratio of the robot arm with T as the sampling period, and the high-frequency amplitude ratio I ω It can be expressed as:

[0110]

[0111] in, can be the sum corresponding to the high frequency amplitude, The sum corresponding to the total amplitude and frequency can be obtained.

[0112] The bone density detection device can determine whether the high-frequency amplitude ratio is greater than a preset ratio threshold. If so, it determines that the motion state of the robotic arm is an unstable state, indicating that the current control parameters are not compatible with the current bone density. The current control parameters of the robotic arm are adjusted manually or automatically by the robotic arm to obtain corrected control parameters that are compatible with the current bone density.

[0113] Step 1002: divide the current interval into two correction intervals according to the current bone density, perform interpolation processing on the two correction intervals based on the current bone density and the correction control parameter, and obtain a correction interpolation function corresponding to each correction interval.

[0114] The correction interval may be a new interval obtained by dividing the current interval with the current bone density as the new interval endpoint value. For example, if the current interval is 5 to 8 and the current bone density is 7, the correction interval may include 5 to 7 and 7 to 8. The correction interpolation function may be an interpolation function determined based on the interval endpoint values ​​of the correction interval and the corresponding control parameters.

[0115] In this embodiment, the current interval has a left endpoint value and a right endpoint value, the left endpoint value corresponds to the left control parameter, and the right endpoint value corresponds to the right control parameter. The bone density detection device can use the left endpoint value and the current bone density as the endpoint values ​​of the interval to establish a correction interval on the left. For the correction interval on the left, interpolation processing is performed according to the left control parameter corresponding to the left endpoint value and the correction control parameter corresponding to the current bone density, and a correction interpolation function corresponding to the correction interval on the left is obtained. In addition, the bone density detection device can use the current bone density and the right endpoint value as the endpoint values ​​of the interval, and interpolation processing is performed according to the right control parameter corresponding to the right endpoint value and the correction control parameter corresponding to the current bone density, and a correction interpolation function corresponding to the correction interval on the right is obtained.

[0116] Step 1003: Update the conversion interpolation function corresponding to the current interval into two revised interpolation functions corresponding to two revised intervals.

[0117] In this embodiment, the bone density detection device can update the conversion interpolation function corresponding to the current interval among the multiple conversion interpolation functions to the correction interpolation function corresponding to the two correction intervals, so that the control parameters are no longer determined based on the conversion interpolation function corresponding to the current interval, but based on the two correction interpolation functions corresponding to the two correction intervals.

[0118] In the above scheme, by adopting the interpolation method for fitting, while ensuring the priority use of the control parameters in the experiment, it also allows the conversion interpolation function to be adjusted in real time during the use of the robotic arm, so that the control effect of the interpolation function on the robotic arm can be continuously optimized during the use of the robotic arm.

[0119] Next, the bone density detection method in the embodiment of the present disclosure is further explained through a specific example.

[0120] First, read the CT image to be processed. Further, determine the target muscle area and target fat area in the CT image to be processed, and establish the density conversion relationship between the pixel CT value and density. Based on the density conversion relationship, determine the bone density distribution of the part to be operated according to the CT value of the part to be operated. According to the bone density distribution, control the motion of the robotic arm.

[0121] In some embodiments of the present disclosure, the motion control of the robotic arm is performed according to the bone density distribution, including:

[0122] Step a1, select part of the bone density recorded in the bone density distribution as the interval endpoint value, use preset materials to simulate the bones of the interval endpoint value of the bone density, and obtain the calibration control parameters corresponding to the interval endpoint value.

[0123] Step a2: establish corresponding bone density intervals according to adjacent interval endpoint values, perform interpolation processing on each bone density interval, and obtain a conversion interpolation function that corresponds one-to-one to the bone density interval.

[0124] Step a3, when using the robotic arm to operate the target bone, if the bone density is the same as the interval endpoint value, the corresponding calibration control parameters are used to control the motion of the robotic arm; if the bone density is different from the interval endpoint value, the corresponding conversion interpolation function is used to determine the control parameters corresponding to the bone density.

[0125] Step a4, when the motion state of the robot arm at the current position point is an unstable state, the current control parameters are adjusted, and the current control parameters corresponding to the current interval are adjusted to two corrected interpolation functions corresponding to two correction intervals, and then the updated interpolation function is used to control the motion of the robot arm.

[0126] Fig.11 A structural schematic diagram of a bone density detection device provided in an embodiment of the present disclosure is shown.

[0127] like Fig.11 As shown, the bone density detection device 1100 may include:

[0128] An acquisition module 1101 is used to acquire a to-be-processed computed tomography (CT) image of a target body part;

[0129] A first determination module 1102 is used to determine a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part according to the pixel CT values ​​in the CT image to be processed; wherein the target body tissues are other body tissues in the target body part except the target bones;

[0130] A calculation module 1103 is used to calculate the density conversion relationship corresponding to the CT image to be processed according to the preset tissue densities corresponding to the multiple target body tissues and the tissue CT values; wherein the density conversion relationship represents the relationship between the pixel CT value and the density;

[0131] The second determination module 1104 is used to determine the voxel-level bone density distribution of the part to be operated in the target bone according to the density conversion relationship.

[0132] Optionally, the first determining module 1102 includes:

[0133] A first determination submodule is used to determine a target bone region corresponding to the target bone in the CT image to be processed, and set the CT value of pixels located in the target bone region to 0 to obtain an intermediate CT image;

[0134] A second determination submodule, configured to determine a plurality of target tissue regions in the intermediate CT image based on a plurality of CT value ranges corresponding to the plurality of target body tissues;

[0135] The third determination submodule is configured to determine the multiple tissue CT values ​​based on the pixel CT values ​​within the multiple target tissue regions.

[0136] Optionally, determining a target bone region corresponding to the target bone in the CT image to be processed includes:

[0137] Performing model preprocessing and sliding window processing on the CT three-dimensional model to obtain multiple three-dimensional sub-models; wherein the model preprocessing includes voxel point distance unification processing and pixel CT value normalization processing; wherein the CT three-dimensional model is a model obtained by three-dimensional reconstruction based on the CT image to be processed;

[0138] Inputting the multiple three-dimensional sub-models into the trained bone segmentation model to obtain multiple characteristic sub-models of the target bone output by the bone segmentation model;

[0139] The three-dimensional bone region corresponding to the target bone in the three-dimensional CT model is determined according to the multiple feature sub-models, and the three-dimensional bone region is dimensionally converted to obtain the target bone region.

[0140] Optionally, the second determining submodule is used to:

[0141] For each of the target body tissues, binarizing the intermediate CT image according to the CT value range corresponding to the target body tissue to obtain an original mask of the target body tissue;

[0142] The original mask is processed according to a preset region stitching algorithm to obtain a target mask, and the extraction region corresponding to the target mask is determined as the target tissue region.

[0143] Optionally, the third determining submodule is used to:

[0144] For each target tissue region, an inscribed region of a preset shape within the target tissue region is determined, and the tissue CT value is calculated according to the pixel CT values ​​within the inscribed region.

[0145] Optionally, the bone density distribution is used to record a plurality of bone densities corresponding to a plurality of position points in the part to be operated, and the device further comprises:

[0146] A third determination module is used to determine each of the position points as a current position point, and determine the bone density interval where the current bone density of the current position point is located as the current interval among multiple bone density intervals; wherein the interval endpoint values ​​corresponding to the multiple bone density intervals are some values ​​selected from the multiple bone densities;

[0147] A conversion module, used for converting the current bone density according to the conversion interpolation function corresponding to the current interval to obtain the current control parameter corresponding to the current position point; wherein the conversion interpolation function is determined by interpolating the interval endpoint value of the current interval and the calibration control parameter corresponding to the interval endpoint value;

[0148] A control module is used to control the robot arm according to the current control parameters.

[0149] Optionally, the device further comprises:

[0150] an adjustment module, configured to adjust parameters of the robotic arm after controlling the robotic arm according to the current control parameters, if the motion state of the robotic arm at the current position point is an unstable state, to obtain a corresponding corrected control parameter when the motion state at the current position point is a stable state;

[0151] An interpolation module, used for dividing the current interval into two correction intervals according to the current bone density, and performing interpolation processing on the two correction intervals based on the current bone density and the correction control parameter to obtain a correction interpolation function corresponding to each correction interval;

[0152] An updating module is used to update the conversion interpolation function corresponding to the current interval into two revised interpolation functions corresponding to the two revised intervals.

[0153] It should be noted that Fig.11 The bone density detection device 1100 shown can execute each step in the above-mentioned bone density detection method embodiment, and realize each process and effect in the above-mentioned bone density detection method embodiment, which will not be described in detail here.

[0154] Fig.12 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown.

[0155] like Fig.12 As shown, the electronic device may include a processor 1201 and a memory 1202 storing computer program instructions.

[0156] Specifically, the processor 1201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0157] The memory 1202 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 1202 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 1202 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 1202 may be inside or outside the integrated gateway device. In a specific embodiment, the memory 1202 is a non-volatile solid-state memory. In a specific embodiment, the memory 1202 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically alterable ROM (EAROM) or flash memory, or a combination of two or more of these.

[0158] The processor 1201 reads and executes the computer program instructions stored in the memory 1202 to perform the steps of the bone density detection method provided in the embodiment of the present disclosure.

[0159] In one example, the electronic device may further include a transceiver 1203 and a bus 1204. Fig.12 As shown, the processor 1201, the memory 1202 and the transceiver 1203 are connected via a bus 1204 and communicate with each other.

[0160] The bus 1204 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 1204 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0161] The following is an embodiment of a computer-readable storage medium provided in an embodiment of the present disclosure. The computer-readable storage medium and the bone density detection method of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the computer-readable storage medium, reference can be made to the embodiment of the above-mentioned bone density detection method.

[0162] This embodiment provides a storage medium containing computer executable instructions, which are used to perform a bone density detection method when executed by a computer processor.

[0163] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided in an embodiment of the present disclosure are not limited to the above method operations, and can also execute related operations in the bone density detection method provided in any embodiment of the present disclosure.

[0164] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present disclosure can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer cloud platform (which can be a personal computer, server, or network cloud platform, etc.) to execute the bone density detection method provided by each embodiment of the present disclosure.

[0165] Note that the above are only preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art will understand that the present disclosure is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present disclosure. Therefore, although the present disclosure is described in more detail through the above embodiments, the present disclosure is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present disclosure, and the scope of the present disclosure is determined by the scope of the appended claims.

Claims

1. A method for detecting bone density, characterized in that: include: Acquiring a computerized tomography (CT) image of a target body part to be processed; Determine, according to the pixel CT values ​​in the CT image to be processed, a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part; wherein the target body tissues are other body tissues in the target body part except the target bones; Calculating a density conversion relationship corresponding to the CT image to be processed according to the preset tissue densities corresponding to the multiple target body tissues and the tissue CT values; wherein the density conversion relationship represents the relationship between the pixel CT value and the density; According to the density conversion relationship, the voxel-level bone density distribution of the part to be operated in the target bone is determined.

2. The method according to claim 1, characterized in that The step of determining a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part according to the pixel CT values ​​in the CT image to be processed comprises: Determine a target bone region corresponding to the target bone in the CT image to be processed, set the CT value of pixels located in the target bone region to 0, and obtain an intermediate CT image; Determining a plurality of target tissue regions in the intermediate CT image based on a plurality of CT value ranges corresponding to the plurality of target body tissues; Based on the pixel CT values ​​within the multiple target tissue regions, the multiple tissue CT values ​​are determined.

3. The method according to claim 2, characterized in that The determining of a target bone region corresponding to the target bone in the CT image to be processed includes: Performing model preprocessing and sliding window processing on the CT three-dimensional model to obtain multiple three-dimensional sub-models; wherein the model preprocessing includes voxel point distance unification processing and pixel CT value normalization processing; wherein the CT three-dimensional model is a model obtained by three-dimensional reconstruction based on the CT image to be processed; Inputting the multiple three-dimensional sub-models into the trained bone segmentation model to obtain multiple characteristic sub-models of the target bone output by the bone segmentation model; The three-dimensional bone region corresponding to the target bone in the three-dimensional CT model is determined according to the multiple feature sub-models, and the three-dimensional bone region is dimensionally converted to obtain the target bone region.

4. The method according to claim 2, characterized in that: The determining of the plurality of target tissue regions in the intermediate CT image based on the plurality of CT value ranges corresponding to the plurality of target body tissues comprises: For each of the target body tissues, binarizing the intermediate CT image according to the CT value range corresponding to the target body tissue to obtain an original mask of the target body tissue; The original mask is processed according to a preset region stitching algorithm to obtain a target mask, and the extraction region corresponding to the target mask is determined as the target tissue region.

5. The method according to claim 2, characterized in that: The determining the multiple tissue CT values ​​based on the pixel CT values ​​within the multiple target tissue regions comprises: For each target tissue region, an inscribed region of a preset shape within the target tissue region is determined, and the tissue CT value is calculated according to the pixel CT values ​​within the inscribed region.

6. The method according to claim 1, characterized in that The bone density distribution is used to record a plurality of bone densities corresponding to a plurality of position points in the part to be operated, and the method further comprises: Determine each of the position points as the current position point, and determine the bone density interval where the current bone density of the current position point is located as the current interval among multiple bone density intervals; wherein the interval endpoint values ​​corresponding to the multiple bone density intervals are some of the values ​​selected from the multiple bone densities; The current bone density is converted according to the conversion interpolation function corresponding to the current interval to obtain the current control parameter corresponding to the current position point; wherein the conversion interpolation function is determined by interpolating the interval endpoint value of the current interval and the calibration control parameter corresponding to the interval endpoint value; The robotic arm is controlled according to the current control parameters.

7. The method according to claim 6, characterized in that After controlling the robotic arm according to the current control parameters, the method further includes: If the motion state of the mechanical arm at the current position point is an unstable state, adjusting the parameters of the mechanical arm to obtain the corresponding corrected control parameters when the motion state of the mechanical arm at the current position point is a stable state; Dividing the current interval into two correction intervals according to the current bone density, and performing interpolation processing on the two correction intervals based on the current bone density and the correction control parameter to obtain a correction interpolation function corresponding to each correction interval; The conversion interpolation function corresponding to the current interval is updated to two revised interpolation functions corresponding to the two revised intervals.

8. A bone density detection device, characterized in that: include: An acquisition module, used for acquiring a to-be-processed computed tomography (CT) image of a target body part; A first determination module is used to determine a plurality of tissue CT values ​​corresponding to a plurality of target body tissues in the target body part according to the pixel CT values ​​in the CT image to be processed; wherein the target body tissues are other body tissues in the target body part except the target bones; A calculation module, configured to calculate a density conversion relationship corresponding to the CT image to be processed according to preset tissue densities corresponding to the plurality of target body tissues and the tissue CT values; wherein the density conversion relationship represents a relationship between the pixel CT value and the density; The second determination module is used to determine the voxel-level bone density distribution of the part to be operated in the target bone according to the density conversion relationship.

9. An electronic device, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method according to any one of claims 1 to 7.

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