Bone strength simulation calculation method and device, and storage medium

A bone strength simulation calculation method, comprising:

• obtaining three-dimensional data of a cancellous bone of a bone segment to be analyzed;
• obtaining a skeleton mechanical model according to the three-dimensional data;
• performing a feature analysis on the skeleton mechanical model to obtain skeleton strength data;
• wherein trabeculae in the cancellous bone are interwoven with each other, and joints of three or more trabeculae are trabecular nodes;
• the skeleton strength data comprises a quantity of the trabecular nodes, and/or data of angle between trabecular bones of the trabecular nodes;
• wherein the performing a feature analysis on the skeleton mechanical model to obtain skeleton strength data, comprises:
• determining a quantity of bone trabeculae constituting each trabecular node in the skeleton mechanical model to obtain a type of each trabecular node;
• extracting a quantity of discriminant trabecular nodes in various types of trabecular nodes;
• extracting at least one type of trabecular node as a pedestal trabecular node;
• determining angles between each bone trabeculae constituting the pedestal trabecular node successively to obtain data of angles between each pedestal trabecular node;
• performing a finite element simulation on the angles between the trabecular bones of the pedestal trabecular nodes to obtain a maximum stress of each angle type;
• wherein the determining a quantity of bone trabeculae constituting each trabecular node in the skeleton mechanical model, comprises:
• marking each pixel of the skeleton mechanical model according to an order of length*width*height l*m*n, wherein a range of mark value is 0 to l*m*n−1;
• extracting pixels whose dark-and-bright indication values meet a preset skeleton threshold in an image of the skeleton mechanical model, marking the pixels as skeleton points;
• traversing the skeleton points of the skeleton mechanical model with a three-dimensional voxel model, extracting with more than three skeleton points at neighboring point positions, and marking the pixel points as candidate nodes;
• pushing coordinates of the candidate nodes into a stack as a seed point for region growth, and setting a tag value of the candidate nodes as a tag value of the candidate nodes;
• performing a neighborhood search on the candidate nodes in the stack successively to determine whether the tag value of the candidate nodes is modified, and if not, popping the candidate nodes out of the stack until a distance between a spread candidate node and a first candidate node is greater than a preset search radius or there is no such spread candidate node;
• removing a candidate node whose distance from the candidate nodes in the stack is less than a preset minimum distance threshold, counting a quantity and coordinates of remaining candidate nodes, and determining a quantity of trabecular bone forming the trabecular node according to the quantity of the remaining candidate nodes;
• wherein the performing a feature analysis on the skeleton mechanical model to obtain the skeleton strength data, comprises, applying a first bone strength value calculation formula to obtain the skeleton strength data:
• E
• ⁢
• 1
• =
• e
• ⁢
• σ
• ⁢
• yield
• +
• fK
• +
• gM
• ,
• wherein E1 is the first bone strength value, e, f, and g are coefficients, σyield is the maximum stress of the pedestal trabecular nodes, K is a weight of proportion of the discriminative trabecular nodes, and M is a weight of proportions of various angle modes in the pedestal trabecular nodes.