WO2023010785A1 - 一种骨强度模拟计算方法、装置及存储介质 - Google Patents

一种骨强度模拟计算方法、装置及存储介质 Download PDF

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WO2023010785A1
WO2023010785A1 PCT/CN2021/142019 CN2021142019W WO2023010785A1 WO 2023010785 A1 WO2023010785 A1 WO 2023010785A1 CN 2021142019 W CN2021142019 W CN 2021142019W WO 2023010785 A1 WO2023010785 A1 WO 2023010785A1
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bone
trabecular
skeleton
nodes
strength
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PCT/CN2021/142019
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English (en)
French (fr)
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张立海
张书威
王铁
胡磊
胡颖
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中国人民解放军总医院第一医学中心
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Publication of WO2023010785A1 publication Critical patent/WO2023010785A1/zh
Priority to US18/432,357 priority Critical patent/US20240177303A1/en

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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
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Definitions

  • the invention relates to the technical field of bone strength assessment, in particular to a bone strength simulation calculation method, device and storage medium.
  • DXA can reflect the sum of cortical bone and cancellous bone, and the measurement result is area bone density in g/cm2;
  • the method of QCT is to use conventional CT plus phantom, scan the lumbar spine and the reference phantom below it simultaneously, and then
  • the region of interest (ROI) is defined as the trabecular bone in the cancellous bone in the middle layer of each vertebral body, and the bone density value of the cancellous bone of each vertebral body can be obtained through computer processing and analysis , and then further calculate the average value of the measured lumbar bone density, which can separate the cortical bone from the cancellous bone and measure the real cancellous bone density.
  • the measurement result is the volume bone density, and the unit is g/cm3.
  • DXA is a two-dimensional bone density measurement, which reflects the area density rather than the real volume, that is, the bone density measured by DXA is the sum of all the bones in the scanning area, and the cortical bone and cancellous bone cannot be distinguished. It will reduce the sensitivity of observing treatment changes; and the bone density measured by DXA is obviously affected by the geometric shape, so even if the actual bone density is the same, if DXA is used, the thick bone is higher than the thin bone density; DXA measurement Spine BMD is the area density of the entire vertebral body including the vertebral body and the vertebral arch.
  • Aortic calcification, degenerative osteoarthrosis, bone hyperplasia, spinous processes, calluses, and compression fractures can all lead to increased BMD.
  • QCT can obtain the bone density value of the cancellous bone of each vertebral body, and the unit is then further calculate the average value of the measured lumbar bone density, the unit is g/cm3, which is a simple density value, although it can solve the problem of DXA Part of the problem, but the density value can only roughly evaluate the bone strength, and cannot fully reflect the bone strength.
  • the present invention urgently needs to provide a new bone strength simulation calculation method, device and storage medium.
  • the purpose of the present invention is to provide a new bone strength simulation calculation method, device and storage medium. Starting from the mechanical structural factors affecting bone strength, the mechanical strength of the skeleton is calculated by numerical simulation to solve the problems existing in the prior art. Bone density evaluates bone strength, resulting in technical problems that cannot perfectly reflect bone strength.
  • One aspect of the present invention provides a bone strength simulation calculation method, comprising the following steps:
  • the acquisition of the three-dimensional data of the spongy bone of the bone segment to be analyzed specifically includes:
  • the three-dimensional data of cancellous bone in the bone segment to be analyzed was obtained by using the QCT measurement method.
  • the skeleton mechanical model is obtained according to the three-dimensional data, which specifically includes:
  • the bone trabeculae in the cancellous bone are interwoven, and the joints of three or more bone trabeculae are trabecular nodes;
  • the skeleton strength data includes data on the number of trabecular nodes, and/or data on the angle between trabecular bones of the trabecular nodes.
  • skeleton mechanical model performing feature analysis on the skeleton mechanical model to obtain skeleton strength data, including:
  • the quantity data of discriminant trabecular nodes among various types of trabecular nodes are extracted.
  • discriminative trabecular nodes include three-pronged nodes, four-pronged nodes and five-pronged nodes.
  • skeleton mechanical model performing feature analysis on the skeleton mechanical model to obtain skeleton strength data, including:
  • the included angles between the bone trabeculae constituting the trabecular nodes of the pedestal are judged one by one to obtain the included angle data of each trabecular node of the pedestal.
  • the characteristic analysis of the skeleton mechanical model to obtain the skeleton strength data also includes:
  • a finite element simulation is performed on the included angles between the trabecular bone nodes of the trabecular nodes of the pedestal to obtain the maximum stress of each included angle type.
  • the extraction of at least one type of trabecular node as the pedestal trabecular node is specifically:
  • a type of trabecular node with the largest number among various types of trabecular nodes is extracted as the pedestal trabecular node.
  • pedestal trabecular joints include trifurcated joints.
  • the judging the number of trabecular bone constituting each trabecular node in the skeleton mechanical model specifically includes:
  • Neighborhood searches are performed on the candidate nodes in the stack in turn to determine whether the tag value of the candidate node has been modified, and if not, pop the candidate node out of the stack; until The distance between the spread candidate node and the first candidate node is greater than the preset search radius or there is no candidate node that can continue to spread;
  • E 1 is the first bone strength value
  • e, f, and g are coefficients
  • ⁇ yield is the maximum stress of the trabecular nodes of the pedestal
  • K is the weighting of the proportion of the trabecular nodes of each of the discriminative classes
  • M is the weight for the proportion of each different angular mode in the trabecular nodes of the pedestal.
  • it also includes applying a machine learning method to train the first bone strength value calculation formula.
  • the method for obtaining training samples includes:
  • the training bone segment and the first bone strength marker value of the training bone segment are used as training samples.
  • obtaining comprehensive bone strength data according to the skeleton strength data and the bone density data includes, applying the following comprehensive bone strength calculation formula to obtain the comprehensive bone strength data:
  • E is the comprehensive bone strength value
  • a, b, c, d are coefficients
  • T is bone density data
  • ⁇ yield is the maximum stress of the trabecular nodes of the pedestal
  • K is the maximum stress of the trabecular nodes of each of the discriminative classes.
  • the weighting of point ratio, M is the weighting of the ratio of different angle modes in the trabecular nodes of the pedestal.
  • it also includes applying a machine learning method to train the formula for calculating the comprehensive bone strength value.
  • the method for obtaining training samples includes:
  • the training bone segment and the comprehensive bone strength marker value of the training bone segment are used as training samples.
  • a bone strength simulation computing device which includes a processor and a memory, wherein computer instructions are stored in the memory, and the processor is used to execute the instructions stored in the memory.
  • Computer instructions when the computer instructions are executed by the processor, the device implements the steps of any one of the above methods.
  • a computer storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method steps described in any one of the above items are implemented.
  • a bone strength simulation calculation method provided by the present invention adopts reconstruction based on the three-dimensional data to obtain the skeleton mechanical model, and then conducts characteristic analysis on the skeleton mechanical model to obtain the skeleton strength data, which is used to assist in judging the
  • the design of the strength of the bone segment to be analyzed is simulated and reconstructed through the data of the bone segment to be analyzed, and then the characteristics of the reconstructed skeleton mechanical model are analyzed.
  • Young's elastic modulus the greater the strength, the smaller the elasticity, can be The strength of the skeletal structure can be effectively obtained, thereby providing strong evidence for the subsequent judgment of bone strength, and increasing the accuracy and reliability of bone strength assessment results.
  • the present invention obtains a skeleton mechanical model, which specifically includes: performing three-dimensional reconstruction according to the three-dimensional data to obtain the spatial structure of the trabecular bone; skeletonizing the obtained spatial structure of the trabecular bone processing to obtain a mechanical model of bone trabeculae, that is, the design of the skeleton mechanical model.
  • the skeletonized mechanical model shows a more concise skeleton structure, which eliminates the influence of factors such as bone trabecular thickness on the structural strength; at the same time, it reduces the amount of calculation and reduces the skeleton strength data without affecting the skeleton strength data as much as possible.
  • the calculation time is reduced, and the requirements for the computer hardware system are also reduced when the method is applied.
  • the present invention adopts the data of said skeleton strength including the quantity data of trabecular nodes, and/or, the design of the angle data between trabecular bones of trabecular nodes, and extracts the trabecular nodes that have the greatest influence on bone strength.
  • the number and the angle between trabecular bones of trabecular nodes are used as skeleton strength data, which has a high correlation with bone strength and makes the evaluation results accurate; the number data of trabecular nodes or the inter-trabecular bone
  • the included angle data are the main structural factors affecting the skeleton strength. The more types of skeleton strength data are selected, the more comprehensive the bone strength evaluation will be.
  • the bone strength simulation calculation method provided by the present invention also includes: obtaining the bone density data of the spongy bone of the bone segment to be analyzed; the design of the step of obtaining comprehensive bone strength data according to the skeleton strength data and the bone density data, Combining skeleton strength data with bone density data, comprehensively utilizing the internal characteristics of each septum that affects bone strength to calculate comprehensive bone strength data, makes the evaluation of bone strength more objective and comprehensive, and effectively improves the accuracy and accuracy of bone strength evaluation. credibility.
  • Fig. 1 is the flowchart of the method for assessing bone strength described in Embodiment 1 of the present invention
  • FIG. 2 is a schematic diagram of the spatial structure of the trabecular bone described in Embodiment 1 of the present invention.
  • FIG. 3 is a schematic structural view of the skeleton mechanical model described in Embodiment 1 of the present invention.
  • FIG. 4 is a schematic structural view of the trabecular nodes described in Embodiment 1 of the present invention.
  • FIG. 5 is a schematic structural diagram of the three-dimensional voxel model described in Embodiment 1 of the present invention.
  • Fig. 6 is a schematic structural diagram of the first model described in Embodiment 1 of the present invention.
  • FIG. 8 is a flow chart of the bone strength assessment method described in Embodiment 2 of the present invention.
  • connection should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection. Connected, or integrally connected; it can be mechanically connected or electrically connected; it can be directly connected or indirectly connected through an intermediary, and it can be the internal communication of two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention in specific situations.
  • a bone strength simulation calculation method provided in this embodiment includes the following steps:
  • the bone strength simulation calculation method uses reconstruction based on the three-dimensional data to obtain the skeleton mechanical model, and then performs feature analysis on the skeleton mechanical model to obtain the skeleton strength data, which is used to assist in judging the bone segment to be analyzed
  • the strength of the structure can provide strong evidence for the subsequent judgment of bone strength and increase the accuracy and reliability of bone strength assessment results.
  • the specific process of acquiring the three-dimensional data of the spongy bone of the bone segment to be analyzed can be directly extracting the three-dimensional data of the spongy bone from the stored medical image data, or can be acquired through any existing method.
  • the detection means of the three-dimensional data detects the bone segment to be analyzed to obtain the three-dimensional data of the cancellous bone, and those skilled in the art can obtain the current method according to the actual needs.
  • the acquisition of the three-dimensional data of the cancellous bone of the bone segment to be analyzed specifically includes:
  • the three-dimensional data of cancellous bone in the bone segment to be analyzed was obtained by using the QCT measurement method.
  • skeleton mechanics model specifically include:
  • the present invention obtains a skeleton mechanical model based on the three-dimensional data, which specifically includes: performing three-dimensional reconstruction according to the three-dimensional data to obtain the spatial structure of the trabecular bone; performing skeletonization processing on the obtained spatial structure of the trabecular bone, A mechanical model of bone trabecular is obtained, that is, the design of the skeleton mechanical model.
  • the skeletonized mechanical model shows a more concise skeleton structure, which eliminates the influence of factors such as bone trabecular thickness on the structural strength; at the same time, it reduces the amount of calculation and reduces the skeleton strength data without affecting the skeleton strength data as much as possible.
  • the calculation time is reduced, and the requirements for the computer hardware system are also reduced when the method is applied.
  • the trabecular bone 111 in the cancellous bone interweaves with each other, and the joints of three or more trabecular bone are trabecular nodes;
  • the skeleton strength data includes the data of the number of trabecular nodes, and/or the data of the angle between trabecular bones of the trabecular nodes.
  • the present invention adopts the data of the number of trabecular nodes included in the skeleton strength data, and/or, the design of the angle data between the trabecular bone of the trabecular nodes, and extracts the quantity and
  • the angle between trabecular bones of trabecular nodes is used as the skeleton strength data, which has the characteristics of high correlation with bone strength and makes the evaluation results accurate; the number data of trabecular nodes or the angle between trabecular bones of trabecular nodes
  • the data are the main structural factors that affect the skeleton strength, the more types of skeleton strength data are selected, the more comprehensive the bone strength evaluation will be. Certainly, the more skeleton strength data are selected, the greater the calculation amount, and those skilled in the art can select the types included in the skeleton strength data according to actual needs.
  • skeleton strength data comprises:
  • the quantity data of discriminant trabecular nodes among various types of trabecular nodes are extracted.
  • the judging the number of trabecular bones constituting each trabecular node in the skeleton mechanical model to obtain the type of each trabecular node is: judging the bone density of each trabecular node in the skeleton mechanical model one by one.
  • the number of trabeculae; of course, the type of trabecular nodes is determined according to the number of bone trabeculae that make up the trabecular nodes, and there are usually three-pointed nodes 101, four-pointed nodes 102, and five-pointed nodes 103 in the cancellous bone , six fork nodes and other types.
  • the discriminative trabecular nodes in this embodiment include three-pronged nodes, four-pronged nodes and five-pronged nodes.
  • the skeletal mechanical model is subjected to feature analysis to obtain skeletal strength data, including:
  • the included angles between the bone trabeculae constituting the trabecular nodes of the pedestal are judged one by one to obtain the included angle data of each trabecular node of the pedestal.
  • the feature analysis of the skeleton mechanical model is carried out to obtain the skeleton strength data, which also includes:
  • a finite element simulation is performed on the included angles between the trabecular bone nodes of the trabecular nodes of the pedestal to obtain the maximum stress of each included angle type.
  • Extracting at least one type of trabecular nodes described in this embodiment as pedestal trabecular nodes is specifically: extracting a type of trabecular nodes with the largest number of various types of trabecular nodes as the trabecular nodes of the pedestal Pedestal trabecular nodes.
  • the type of trabecular nodes with the largest number among the trabecular nodes is a three-pronged node, so the pedestal trabecular nodes are selected as three-pronged nodes.
  • the pedestal trabecular nodes include multiple types of trabecular nodes.
  • the determination of the quantity of trabeculae constituting each trabecular node in the skeletal mechanics model described in this embodiment specifically includes:
  • Extracting pixels whose dark and bright indication values meet the preset skeleton threshold in the image of the skeleton mechanical model are marked as skeleton points;
  • Neighborhood searches are performed on the candidate nodes in the stack in turn to determine whether the tag value of the candidate node has been modified, and if not, pop the candidate node out of the stack; until The distance between the spread candidate node and the first candidate node is greater than the preset search radius or there is no candidate node that can continue to spread;
  • the image of the mechanical model of the skeleton is a binary image, the trabecular bone is represented by 1, and the non-trabecular bone position is represented by 0.
  • performing characteristic analysis on the mechanical model of the skeleton to obtain the skeleton strength data including, applying the following calculation formula of the first bone strength value to obtain the skeleton strength data:
  • E 1 is the first bone strength value
  • e, f, and g are coefficients
  • ⁇ yield is the maximum stress of the trabecular nodes of the pedestal
  • K is the weighting of the proportion of the trabecular nodes of each of the discriminative classes
  • M is the weight for the proportion of each different angular mode in the trabecular nodes of the pedestal.
  • This embodiment also includes applying a machine learning method to train the first bone strength calculation formula.
  • the method for obtaining training samples includes:
  • the training bone segment and the first bone strength marker value of the training bone segment are used as training samples.
  • the finite element analysis described in this embodiment is to classify the three-point nodes with different angle ranges according to the type of included angle, and obtain several types of three-point nodes with different angle ranges.
  • CATIA modeling software and ABAQUS mechanical analysis software establish a network structure composed of hexagonal phases to form the first model in which one node connects three rods in space (as shown in Figure 6). Add the cross-sectional area to the rods in the software, and connect them with balls to form the second model representing the solid structure (as shown in Figure 7).
  • the unit cells were taken for finite element analysis.
  • the different types of unit cells have the same density (that is, the number of different types of trifurcated nodes in the unit space remains the same; the unit cell density refers to the ratio of the mass of the entity formed by the connection of "rods" and “balls” to the volume of space), and the boundary is imposed Conditions, the same stress of 0.25Mpa is applied to the upper side.
  • Nodes of different angle types have different strength values, for example:
  • Nodes of different angle types have different mechanical properties. Through categorization, the nodes of different angle types are simulated by finite element, and the maximum stress of each type can be obtained, which forms the evaluation parameters for the description of trident nodes. Similarly, four-pronged joints and five-pronged joints also have different angle composition types and have different mechanical properties. The maximum stress of each type of trabecular joints can be obtained through the above-mentioned finite element simulation method as evaluation data.
  • this embodiment also provides a bone strength simulation calculation method, which also includes:
  • the bone strength simulation calculation method specifically includes the following steps:
  • the method for obtaining the skeleton strength data is the method shown in Embodiment 1, and the specific process will not be repeated here.
  • the bone strength simulation calculation method provided by the present invention also includes: obtaining the bone density data of the spongy bone of the bone segment to be analyzed; obtaining the comprehensive bone strength data according to the skeleton strength data and the bone density data. Combining strength data with bone density data, comprehensively utilizing the internal characteristics of each septum that affects bone strength to calculate comprehensive bone strength data, making the evaluation of bone strength more objective and comprehensive, and effectively improving the accuracy and credibility of bone strength evaluation Spend.
  • the comprehensive bone strength data is obtained according to the skeleton strength data and the bone density data, including, applying the following comprehensive bone strength calculation formula to obtain the comprehensive bone strength data:
  • E is the comprehensive bone strength value
  • a, b, c, d are coefficients
  • T is bone density data
  • ⁇ yield is the maximum stress of the trabecular nodes of the pedestal
  • K is the maximum stress of the trabecular nodes of each of the discriminative classes.
  • the weighting of point ratio, M is the weighting of the ratio of different angle modes in the trabecular nodes of the pedestal.
  • This embodiment also includes applying a machine learning method to train the formula for calculating the comprehensive bone strength value.
  • the method for obtaining training samples when applying the machine learning method to train the formula for calculating the comprehensive bone strength value, includes:
  • the training bone segment and the comprehensive bone strength marker value of the training bone segment are used as training samples.
  • the present invention also provides a bone strength simulation computing device, which includes a processor and a memory, wherein computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory.
  • the device implements the steps of any one of the methods described above.
  • the present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method steps described in any one of the above items are realized.

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Abstract

一种骨强度模拟计算方法、装置和存储介质。骨强度模拟计算方法，包括获取待分析骨段骨松质的三维数据(S11)；根据所述三维数据，得到骨架力学模型(S12)；对所述骨架力学模型进行特征分析，得到骨架强度数据(S13)。该方法从影响骨强度的力学结构因素出发，通过数值模拟的方法计算骨架力学强度，以解决现有技术中存在的用骨密度评价骨强度，造成不能完善的体现骨强度的技术问题。

Description

一种骨强度模拟计算方法、装置及存储介质 技术领域
本发明涉及骨强度评估技术领域，尤其是涉及一种骨强度模拟计算方法、装置及存储介质。
背景技术
骨矿含量测量的方法很多，而运用最多的是定量计算机体层摄影(quantitaitive computed tomography，简称QCT)和双能X线吸收法(dual energy X-ray absorptiometry，简称DXA)。DXA可反映骨皮质和骨松质的总和，测量结果为面积骨密度，单位为g/cm2；QCT其方法是使用常规CT加体模，通过对腰椎和其下方的参照体模同时扫描，然后在CT图像上将感兴趣区域(region of interest，ROI)定在每节椎体中部层面的骨松质区小梁骨，经计算机处理分析可得出每节椎体骨松质的骨密度值，然后再进一步计算出被测腰椎骨密度的平均值，可以将皮质骨与骨松质分开，测量真正的骨松质密度，测量结果为体积骨密度，单位为g/cm3。
现有评价骨强度都是根据骨密度值粗略评价骨强度，均存在缺陷。DXA是二维骨密度测量，反映的是面积密度而不是真正的体积，即DXA所测得的骨密度为扫描区内所有骨的总和，不能把骨皮质和骨松质区分开，骨皮质的存在会降低观察治疗变化的敏感性；并且DXA所测的骨密度明显受几何形状的影响，所以即使实际骨密度是一样的，如果用DXA，厚的骨比薄的骨密度要高；DXA测定脊柱骨密度是包括椎体和椎弓整个椎体面积密度，主动脉钙化、退行性骨关节病、骨质增生、棘突、骨痂和压缩性骨折都会导致骨密度增高。QCT可得出每节椎体骨松质的骨密度值，其单位为然后 再进一步计算出被测腰椎骨密度的平均值，单位为g/cm3，是单纯的密度值，虽然能够解决DXA的部分问题，但密度值只能粗略评价骨强度，并不能完善的体现骨强度。
因此，针对上述问题本发明急需提供一种新的骨强度模拟计算方法、装置及存储介质。
发明内容
本发明的目的在于提供一种新的骨强度模拟计算方法、装置及存储介质，从影响骨强度的力学结构因素出发，通过数值模拟的方法计算骨架力学强度，以解决现有技术中存在的用骨密度评价骨强度，造成不能完善的体现骨强度的技术问题。
本发明的一个方面，提供了一种骨强度模拟计算方法，包括以下步骤：
获取待分析骨段骨松质的三维数据；
根据所述三维数据，得到骨架力学模型；
对所述骨架力学模型进行特征分析，得到骨架强度数据。
进一步地，所述获取待分析骨段骨松质的三维数据，具体包括：
应用QCT测量方法得到待分析骨段骨松质的三维数据。
进一步地，所述根据所述三维数据，得到骨架力学模型，具体包括：
根据所述三维数据进行三维重建，得到骨小梁的空间结构；
对得到的骨小梁的空间结构进行骨架化处理，得到骨小梁的力学模型，即所述骨架力学模型。
进一步地，骨松质内的骨小梁间相互交织，三条及以上骨小梁的相连接处为小梁结点；
所述骨架强度数据包括小梁结点的数量数据，和/或，小梁结点的骨小梁间夹角数据。
进一步地，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，得到各小梁结点的类型；
提取各类型的小梁结点中判别类小梁结点的数量数据。
进一步地，所述判别类小梁结点包括三叉结点、四叉结点以及五叉结点。
进一步地，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
提取至少一种类型的小梁结点，作为基架小梁结点；
逐个判断构成该基架小梁结点的各骨小梁间的夹角，得到各个所述基架小梁结点的夹角数据。
进一步地，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，还包括：
对所述基架小梁结点的各骨小梁间的夹角进行有限元模拟，得到各个夹角类型的最大应力。
进一步地，所述提取至少一种类型的小梁结点，作为基架小梁结点，具体为：
提取各类型的小梁结点中数量最多的一类小梁结点，作为所述基架小梁结点。
进一步地，所述基架小梁结点，包括三叉结点。
进一步地，所述判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，具体包括：
依据长*宽*高为l*m*n的顺序标记所述骨架力学模型的每一个像素点，标记值的范围为0至l*m*n-1；
提取所述骨架力学模型 的图像中暗亮指示值符合预设骨架阈值的像素点，标记为骨架点；
以三维体素模型遍历所述骨架力学模型的骨架点，提取邻域点位置处具有三个以上骨架点的像素点，标记为候选结点；
将所述候选结点的坐标压入堆栈中，作为区域增长的种子点，并且将所述候选结点的标记值设为候选结点的标记值；
依次对所述堆栈中的所述候选结点进行邻域搜索，判断所述所述候选结点的标记值是否经过修改，若没有经过修改，则将所述候选结点弹出所述堆栈；直到蔓延的所述候选结点到第一个所述候选结点的距离大于预设搜索半径或者没有可以继续蔓延的所述候选结点；
将所述堆栈中距离候选结点的距离小于预设最小距离阈值的所述候选结点剔除，统计剩余所述候选结点的数量和坐标，依据数量确定构成该小梁结点的骨小梁的数量。
进一步地，对所述骨架力学模型进行特征分析，得到骨架强度数据，包括，应用下述第一骨强度值计算公式得出所述骨架强度数据：
E 1＝eσ yield+fK+gM，
其中，E 1为第一骨强度值，e、f、g为系数，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
进一步地，还包括，应用机器学习方法对所述第一骨强度值计算公式进行训练。
进一步地，在应用机器学习方法对所述第一骨强度值计算公式进行训练时，训练样本的获取方法包括：
对训练骨段进行影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
根据几何特征计算结果、有限元分析结果以及力学实验结果得到第一骨强度标记值；
将所述训练骨段以及该训练骨段的第一骨强度标记值作为训练样本。
进一步地，还包括：
获取待分析骨段骨松质的骨密度数据；
根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据。
进一步地，根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据，包括，应用下述综合骨强度值计算公式得出所述综合骨强度数据：
E＝aT+bσ yield+cK+dM；
其中，E为综合骨强度值，a、b、c、d为系数，T为骨密度数据，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
进一步地，还包括，应用机器学习方法对所述综合骨强度值计算公式进行训练。
进一步地，在应用机器学习方法对所述综合骨强度值计算公式进行训练时，训练样本的获取方法包括：
对训练骨段进行骨密度计算以及影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
根据骨密度计算结果、几何特征计算结果、有限元分析结果以及力学实验结果得到综合骨强度标记值；
将所述训练骨段以及该训练骨段的综合骨强度标记值作为训练样本。
本发明的另一方面，提供了一种骨强度模拟计算装置，该装置包括处理器和存储器，其特征在于，所述存储器中存储有计算机指令，所述处理器用于执行所述存储器中存储的计算机指令，当所述计算机指令被处理器执行时该装置实现如上任一项所述方法的步骤。
本发明的再一方面，提供了一种计算机存储介质，其上存储有计算机程序，在该计算机程序被处理器执行时实现如上任一项所述的方法步骤。
本发明与现有技术相比具有以下进步：
1、本发明提供的一种骨强度模拟计算方法采用依据所述三维数据重建得到所述骨架力学模型，再对所述骨架力学模型进行特征分析得到所述骨架强度数据，用于辅助判断所述待分析骨段的强度的设计，通过将待分析骨段的数据进行模拟重建，再对重建后的骨架力学模型进行特征分析，根据杨氏弹性模量，强度越大弹性越小的理论，可以有效得出骨架结构的强度，从而为后续判断骨强度提供有力的证据，增加骨强度评估结果的准确度和可信度。
2、本发明采用所述根据所述三维数据，得到骨架力学模型，具体包括：根据所述三维数据进行三维重建，得到骨小梁的空间结构；对得到的骨小梁的空间结构进行骨架化处理，得到骨小梁的力学模型，即所述骨架力学模型的设计。骨架化的力学模型显示的骨架结构更简明，消除了骨小梁厚度等因素对结构强度的影响；同时，在尽量不影响骨架强度数据的情况下，减小了计算量，减少了骨架强度数据的计算时间，也降低应用本方法时对计算机硬件系统的要求。
3、本发明采用所述骨架强度数据包括小梁结点的数量数据，和/或，小梁结点的骨小梁间夹角数据的设计，提取对骨强度影响最大的小梁结点的数量和小梁结点的骨小梁间夹角作为骨架强度数据，具有与骨强度关联性高，使评价结果准确的特点；小梁结点的数量数据或者小梁结点的骨小梁间夹角数据均是影响骨架强度的主要结构因素，选择的骨架强度数据类型越多，对骨强度的评价越全面。
4、本发明提供的骨强度模拟计算方法采用还包括：获取待分析骨段骨松质的骨密度数据；根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据的步骤的设计，将骨架强度数据与骨密度数据相结合，综合利用影响骨强度的各个骨隔内部特征计算综合骨强度数据，使对骨强度的评价更客观、更全面，有效提高了骨强度评估的准确度和可信度。
附图说明
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明的实施例一中所述骨强度评估方法的流程图；
图2为本发明的实施例一中所述骨小梁的空间结构的示意图；
图3为本发明的实施例一中所述骨架力学模型的结构示意图；
图4为本发明的实施例一中所述小梁结点的结构示意图；
图5为本发明的实施例一中所述三维体素模型的结构示意图；
图6为本发明的实施例一中所述第一模型的结构示意图；
图7为本发明的实施例一中所述第二模型的结构示意图；
图8为本发明的实施例二中所述骨强度评估方法的流程图。
具体实施方式
下面将结合附图对本发明的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发 明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
在本发明的描述中，需要说明的是，术语“中心”、“上”、“下”、“左”、“右”、“竖直”、“水平”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。
在本发明的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本发明中的具体含义。
实施例一
参见图1、图2、图3所示，本实施例提供的一种骨强度模拟计算方法，包括以下步骤：
S11,获取待分析骨段骨松质的三维数据；
S12，根据所述三维数据，得到骨架力学模型；
S13，对所述骨架力学模型进行特征分析，得到骨架强度数据。
本发明提供的骨强度模拟计算方法采用依据所述三维数据重建得到所述骨架力学模型，再对所述骨架力学模型进行特征分析得到所述骨架强度数据，用于辅助判断所述待分析骨段的强度的设计，通过将待分析骨段的数据进行模拟重建，再对重建后的骨架力学模型进行特征分析，根据杨 氏弹性模量，强度越大弹性越小的理论，可以有效得出骨架结构的强度，从而为后续判断骨强度提供有力的证据，增加骨强度评估结果的准确度和可信度。
本发明中，所述获取待分析骨段骨松质的三维数据的具体过程可以为从与存储的医学影像数据中直接提取骨松质的三维数据，也可以通过任何现有的获取骨松质三维数据的检测手段对待分析骨段进行检测，以获取骨松质的三维数据，本领域技术人员可以根据实际需要现在获取的方式。当然为了获取较准确的待分析骨段的骨松质三维数据，所述获取待分析骨段骨松质的三维数据，具体包括：
应用QCT测量方法得到待分析骨段骨松质的三维数据。
参见图2、图3所示，本实施例中所述根据所述三维数据，得到骨架力学模型，具体包括：
根据所述三维数据进行三维重建，得到骨小梁的空间结构；
对得到的骨小梁的空间结构进行骨架化处理，得到骨小梁的力学模型，即所述骨架力学模型。
本发明采用所述根据所述三维数据，得到骨架力学模型，具体包括：根据所述三维数据进行三维重建，得到骨小梁的空间结构；对得到的骨小梁的空间结构进行骨架化处理，得到骨小梁的力学模型，即所述骨架力学模型的设计。骨架化的力学模型显示的骨架结构更简明，消除了骨小梁厚度等因素对结构强度的影响；同时，在尽量不影响骨架强度数据的情况下，减小了计算量，减少了骨架强度数据的计算时间，也降低应用本方法时对计算机硬件系统的要求。
参见图4所示，本实施例中骨松质内的骨小梁111间相互交织，三条及以上骨小梁的相连接处为小梁结点；
所述骨架强度数据，包括，小梁结点的数量数据，和/或，小梁结点的骨小梁间夹角数据。
本发明采用所述骨架强度数据包括小梁结点的数量数据，和/或，小梁结点的骨小梁间夹角数据的设计，提取对骨强度影响最大的小梁结点的数量和小梁结点的骨小梁间夹角作为骨架强度数据，具有与骨强度关联性高，使评价结果准确的特点；小梁结点的数量数据或者小梁结点的骨小梁间夹角数据均是影响骨架强度的主要结构因素，选择的骨架强度数据类型越多，对骨强度的评价越全面。当然，选择的骨架强度数据越多，计算量越大，本领域技术人员可以根据实际需要选择所述骨架强度数据包括的类型。
参见图4所示，本实施例中所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，得到各小梁结点的类型；
提取各类型的小梁结点中判别类小梁结点的数量数据。
所述判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，得到各小梁结点的类型，即为：逐个判断所述骨架力学模型中，各个小梁结点的骨小梁的数量；当然小梁结点的类型是根据构成小梁结点的骨小梁的数量确定的，骨松质内通常有三叉结点101、四叉结点102、五叉结点103、六叉结点等类型。
本实施例中所述判别类小梁结点包括三叉结点、四叉结点以及五叉结点。
本实施例中所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
提取至少一种类型的小梁结点，作为基架小梁结点；
逐个判断构成该基架小梁结点的各骨小梁间的夹角，得到各个所述基架小梁结点的夹角数据。
本实施例中所述对所述骨架力学模型进行特征分析，得到骨架强度数据，还包括：
对所述基架小梁结点的各骨小梁间的夹角进行有限元模拟，得到各个夹角类型的最大应力。
本实施例中所述提取至少一种类型的小梁结点，作为基架小梁结点，具体为：提取各类型的小梁结点中数量最多的一类小梁结点，作为所述基架小梁结点。通常所述小梁结点中数量最多的一类小梁结点为三叉结点，所以选择所述基架小梁结点为三叉结点。当然也可以选择所述基架小梁结点包括多类小梁结点。
参见图4所示，本实施例中所述判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，具体包括：
依据长*宽*高为l*m*n的顺序标记所述骨架力学模型的每一个像素点，标记值的范围为0至l*m*n-1；
提取所述骨架力学模型的图像中暗亮指示值符合预设骨架阈值的像素点，标记为骨架点；
以三维体素模型遍历所述骨架力学模型的骨架点，提取邻域点位置处具有三个以上骨架点的像素点，标记为候选结点；
将所述候选结点的坐标压入堆栈中，作为区域增长的种子点，并且将所述候选结点的标记值设为候选结点的标记值；
依次对所述堆栈中的所述候选结点进行邻域搜索，判断所述所述候选结点的标记值是否经过修改，若没有经过修改，则将所述候选结点弹出所述堆栈；直到蔓延的所述候选结点到第一个所述候选结点的距离大于预设搜索半径或者没有可以继续蔓延的所述候选结点；
将所述堆栈中距离候选结点的距离小于预设最小距离阈值的所述候选结点剔除，统计剩余所述候选结点的数量和坐标，依据数量确定构成该小梁结点的骨小梁的数量。
所述骨架力学模型的图像为二值化图像，骨小梁用1表示，非骨小梁位置用0表示。
本实施例中，对所述骨架力学模型进行特征分析，得到骨架强度数据，包括，应用下述第一骨强度值计算公式得出所述骨架强度数据：
E 1＝eσ yield+fK+gM，
其中，E 1为第一骨强度值，e、f、g为系数，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
本实施例还包括，应用机器学习方法对所述第一骨强度值计算公式进行训练。
本实施例中，在应用机器学习方法对所述第一骨强度值计算公式进行训练时，训练样本的获取方法包括：
对训练骨段进行影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
根据几何特征计算结果、有限元分析结果以及力学实验结果得到第一骨强度标记值；
将所述训练骨段以及该训练骨段的第一骨强度标记值作为训练样本。
参见图6、图7所示，本实施例中所述有限元分析为，根据夹角的类型将具有不同角度范围三叉结点进行分类，得到数种不同角度范围的三叉结点的类型，利用CATIA建模软件和ABAQUS力学分析软件，建立一个六边形相组成的网状结构，形成空间上一个结点连三根杆的第一模型(如图 6所示)。在软件中给杆加上截面积，之间用球相连，形成表示实体结构的第二模型(如图7所示)。
取单胞进行有限元分析。不同的类型单胞密度相同(即不同类型的三叉结点，单位空间内的数量保持一致；单胞密度是指“杆”、“球”连接形成的实体质量与空间体积的比值)，施加边界条件，上侧施加相同应力0.25Mpa。
不同角度类型的结点，强度值不同，例如：
类型一，三个小梁间夹角α＝120°、β＝120°、γ＝120°的三叉结点，最大应力处应力为3.47MPa。
类型二：三个小梁间夹角α＝180°、β＝120°、γ＝90°的三叉结点，最大应力处应力为6.13MPa。
依据上述数据判断骨强度时，可以参考的方法有，最大应力处应力越大，表示此处应力越集中，不易将力传递分散，更容易断裂，当然此处仅是根据理论提出了一种可以评价骨强度的方法，并不对评价骨强度的方法进行限定，对于经过上述方法得到的数据如何应用，本领域技术人员可以根据实际需要进行选择，此处不再过多赘述。
不同角度类型的结点，拥有不同的力学性能，通过归类，将不同角度类型的结点进行有限元模拟，可以得到各个类型的最大应力，即形成了对三叉结点描述的评价参数。同样，四叉结点、五叉结点也存在不同的角度组成类型，拥有不同的力学性能，各类型的小梁结点均可以通过上述有限元模拟方法，得到其最大应力，作为评价数据。
实施例二
参见图8所示，本实施例还提供了一种骨强度模拟计算方法，还包括：
获取待分析骨段骨松质的骨密度数据；
根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据。
此时，所述骨强度模拟计算方法，具体包括以下步骤：
S21,获取待分析骨段骨松质的骨密度数据；
S22，获取待分析骨段骨松质的三维数据；
S23，根据所述三维数据，得到骨架力学模型；
S24，对所述骨架力学模型进行特征分析，得到骨架强度数据；
S25，根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据。
本实施例中，获取骨架强度数据的方法为实施例一中所示的方法，具体过程此处不再多赘述。
本发明提供的骨强度模拟计算方法采用还包括：获取待分析骨段骨松质的骨密度数据；根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据的步骤的设计，将骨架强度数据与骨密度数据相结合，综合利用影响骨强度的各个骨隔内部特征计算综合骨强度数据，使对骨强度的评价更客观、更全面，有效提高了骨强度评估的准确度和可信度。
本实施例中根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据，包括，应用下述综合骨强度值计算公式得出所述综合骨强度数据：
E＝aT+bσ yield+cK+dM；
其中，E为综合骨强度值，a、b、c、d为系数，T为骨密度数据，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
本实施例还包括，应用机器学习方法对所述综合骨强度值计算公式进行训练。
本实施例中，在应用机器学习方法对所述综合骨强度值计算公式进行训练时，训练样本的获取方法包括：
对训练骨段进行骨密度计算以及影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
根据骨密度计算结果、几何特征计算结果、有限元分析结果以及力学实验结果得到综合骨强度标记值；
将所述训练骨段以及该训练骨段的综合骨强度标记值作为训练样本。
本发明还提供了一种骨强度模拟计算装置，该装置包括处理器和存储器，其特征在于，所述存储器中存储有计算机指令，所述处理器用于执行所述存储器中存储的计算机指令，当所述计算机指令被处理器执行时该装置实现如上任一项所述方法的步骤。
本发明还提供了一种计算机存储介质，其上存储有计算机程序，在该计算机程序被处理器执行时实现如上任一项所述的方法步骤。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (20)

  1. 一种骨强度模拟计算方法，其特征在于，包括以下步骤：
    获取待分析骨段骨松质的三维数据；
    根据所述三维数据，得到骨架力学模型；
    对所述骨架力学模型进行特征分析，得到骨架强度数据。
  2. 根据权利要求1所述骨强度模拟计算方法，其特征在于，所述获取待分析骨段骨松质的三维数据，具体包括：
    应用QCT测量方法得到待分析骨段骨松质的三维数据。
  3. 根据权利要求1所述骨强度模拟计算方法，其特征在于，所述根据所述三维数据，得到骨架力学模型，具体包括：
    根据所述三维数据进行三维重建，得到骨小梁的空间结构；
    对得到的骨小梁的空间结构进行骨架化处理，得到骨小梁的力学模型，即所述骨架力学模型。
  4. 根据权利要求1至3中任一项所述骨强度模拟计算方法，其特征在于，骨松质内的骨小梁间相互交织，三条及以上骨小梁的相连接处为小梁结点；
    所述骨架强度数据，包括，小梁结点的数量数据，和/或，小梁结点的骨小梁间夹角数据。
  5. 根据权利要求4所述骨强度模拟计算方法，其特征在于，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
    判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，得到各小梁结点的类型；
    提取各类型的小梁结点中判别类小梁结点的数量数据。
  6. 根据权利要求5所述骨强度模拟计算方法，其特征在于，所述判别类小梁结点包括三叉结点、四叉结点以及五叉结点。
  7. 根据权利要求5所述骨强度模拟计算方法，其特征在于，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，包括：
    提取至少一种类型的小梁结点，作为基架小梁结点；
    逐个判断构成该基架小梁结点的各骨小梁间的夹角，得到各个所述基架小梁结点的夹角数据。
  8. 根据权利要求7所述骨强度模拟计算方法，其特征在于，所述对所述骨架力学模型进行特征分析，得到骨架强度数据，还包括：
    对所述基架小梁结点的各骨小梁间的夹角进行有限元模拟，得到各个夹角类型的最大应力。
  9. 根据权利要求7所述骨强度模拟计算方法，其特征在于，所述提取至少一种类型的小梁结点，作为基架小梁结点，具体为：
    提取各类型的小梁结点中数量最多的一类小梁结点，作为所述基架小梁结点。
  10. 根据权利要求9所述骨强度模拟计算方法，其特征在于，所述基架小梁结点，包括三叉结点。
  11. 根据权利要求5至10中任一项所述骨强度模拟计算方法，其特征在于，所述判断所述骨架力学模型中构成各小梁结点的骨小梁的数量，具体包括：
    依据长*宽*高为l*m*n的顺序标记所述骨架力学模型的每一个像素点，标记值的范围为0至l*m*n-1；
    提取所述骨架力学模型的图像中暗亮指示值符合预设骨架阈值的像素点，标记为骨架点；
    以三维体素模型遍历所述骨架力学模型的骨架点，提取邻域点位置处具有三个以上骨架点的像素点，标记为候选结点；
    将所述候选结点的坐标压入堆栈中，作为区域增长的种子点，并且将所述候选结点的标记值设为候选结点的标记值；
    依次对所述堆栈中的所述候选结点进行邻域搜索，判断所述所述候选结点的标记值是否经过修改，若没有经过修改，则将所述候选结点弹出所述堆栈；直到蔓延的所述候选结点到第一个所述候选结点的距离大于预设搜索半径或者没有可以继续蔓延的所述候选结点；
    将所述堆栈中距离候选结点的距离小于预设最小距离阈值的所述候选结点剔除，统计剩余所述候选结点的数量和坐标，依据数量确定构成该小梁结点的骨小梁的数量。
  12. 根据权利要求11所述骨强度模拟计算方法，其特征在于，对所述骨架力学模型进行特征分析，得到骨架强度数据，包括，应用下述第一骨强度值计算公式得出所述骨架强度数据：
    E 1＝eσ yield+fK+gM，
    其中，E 1为第一骨强度值，e、f、g为系数，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
  13. 根据权利要求12所述骨强度模拟计算方法，其特征在于，还包括，应用机器学习方法对所述第一骨强度值计算公式进行训练。
  14. 根据权利要求13所述骨强度模拟计算方法，其特征在于，在应用机器学习方法对所述第一骨强度值计算公式进行训练时，训练样本的获取方法包括：
    对训练骨段进行影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
    根据几何特征计算结果、有限元分析结果以及力学实验结果得到第一骨强度标记值；
    将所述训练骨段以及该训练骨段的第一骨强度标记值作为训练样本。
  15. 根据权利要求11所述骨强度模拟计算方法，其特征在于，还包括：
    获取待分析骨段骨松质的骨密度数据；
    根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据。
  16. 根据权利要求15所述骨强度模拟计算方法，其特征在于，根据所述骨架强度数据以及所述骨密度数据得到综合骨强度数据，包括，应用下述综合骨强度值计算公式得出所述综合骨强度数据：
    E＝aT+bσ yield+cK+dM；
    其中，E为综合骨强度值，a、b、c、d为系数，T为骨密度数据，σ yield为所述基架小梁结点的最大应力，K为各所述判别类小梁结点比例的加权，M为所述基架小梁结点中各不同角度模式比例的加权。
  17. 根据权利要求12或14所述骨强度模拟计算方法，其特征在于，还包括，应用机器学习方法对所述综合骨强度值计算公式进行训练。
  18. 根据权利要求17所述骨强度模拟计算方法，其特征在于，在应用机器学习方法对所述综合骨强度值计算公式进行训练时，训练样本的获取方法包括：
    对训练骨段进行骨密度计算以及影像扫描，并对扫描后得到的训练骨骼影像进行几何特征计算、有限元分析；对训练用骨段进行力学实验；
    根据骨密度计算结果、几何特征计算结果、有限元分析结果以及力学实验结果得到综合骨强度标记值；
    将所述训练骨段以及该训练骨段的综合骨强度标记值作为训练样本。
  19. 一种骨强度模拟计算装置，其特征在于，该装置包括处理器和存储器，其特征在于，所述存储器中存储有计算机指令，所述处理器用于执 行所述存储器中存储的计算机指令，当所述计算机指令被处理器执行时该装置实现如权利要求1至18中任一项所述方法的步骤。
  20. 一种计算机存储介质，其特征在于，其上存储有计算机程序，在该计算机程序被处理器执行时实现如权利要求1至18中任一项所述的方法步骤。
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