A deep learning pipeline was developed and used to localize and classify a variety of implants in the femur contained in whole-body post-mortem computed tomography (PMCT) scans. The results provide a proof-of-principle approach for labelling content not described in medical/autopsy reports. The pipeline, which incorporated residual networks and an autoencoder, was trained and tested using n = 450 full-body PMCT scans. For the localization component, Dice scores of 0.99, 0.96, and 0.98 and mean absolute errors of 3.2, 7.1, and 4.2 mm were obtained in the axial, coronal, and sagittal views, respectively. A regression analysis found the orientation of the implant to the scanner axis and also the relative positioning of extremities to be statistically significant factors. For the classification component, test cases were properly labelled as nail (N⁺), hip replacement (H⁺), knee replacement (K⁺) or without-implant (I⁻) with an accuracy >97%. The recall for I⁻ and H⁺ cases was 1.00, but fell to 0.82 and 0.65 for cases with K⁺ and N⁺. This semi-automatic approach provides a generalized structure for image-based labelling of features, without requiring time-consuming segmentation.

开发了深度学习管道，并将其用于全身验尸计算机断层扫描（PMCT）扫描中包含的股骨中各种植入物的定位和分类。结果为未在医学/尸检报告中描述的内容加标签提供了原理验证方法。使用n = 450全身PMCT扫描对包含残差网络和自动编码器的管道进行了培训和测试。对于定位组件，分别在轴向，冠状和矢状面获得Dice分数0.99、0.96和0.98，平均绝对误差分别为3.2、7.1和4.2 mm。回归分析发现植入物相对于扫描仪轴的方向以及四肢的相对位置也是统计学上重要的因素。对于分类组件，将测试用例正确标记为指甲（N⁺），髋关节置换（H ⁺），膝关节置换（K ⁺）或无植入（I ^- ），其精确度> 97％。对于我的回忆^-和H ⁺的情况下为1.00，但下降到0.82和0.65用于带K的情况下⁺和N ⁺。这种半自动方法提供了一种通用的结构，用于基于图像的特征标注，而无需耗时的分割。