模板引导与数据驱动的三维碎骨修复方法

针对骨折发生随机、 相互接触碎骨的边界分割困难、 小碎骨丢失导致碎骨拼接困难的问题, 提出一种基于模板引导与数据驱动的三维碎骨修复方法. 首先利用神经网络训练初步提取碎骨外表面, 并计算碎骨与模板外表面特征; 然后基于特征对每个碎骨外表面与骨骼模板进行匹配; 最后根据碎骨与模板间的距离和碎骨表面曲率信息确定断裂边界, 实现碎骨修复拼接. 实验结果表明, 该方法能有效进行碎骨分割与拼接, 分割精度达98.44%, 拼接平移误差为9.218 mm, 旋转误差为1.298°.

Three-Dimensional Broken Bones Repair Method of Template-Guided and Data-Driven

Aiming at the problems that the fracture occurred randomly, it was difficult to segment the boundary of the broken bone in contact with each other, and the loss of the small broken bone led to the difficulty of the broken bone splicing, we proposed a three-dimensional broken bone repair method based on template-guided and data-driven. Firstly, the neural network was used to train initially and extract the external surface of broken bone, and calculate the external surface features of the broken bone and the template. Secondly, based on the features, the external surface of each broken bone was matched with the bone template. Finally, the fracture boundary was determined according to the distance between the broken bone and the template and the curvature information of the broken bone surface, and the broken bone repair and splicing were realized.  The experimental results show that the method can effectively segment and splice the broken bone. The segmentation accuracy is 98.44%, the splicing translation error is 9.218 mm, and the splicing rotation error is 1.298°.