The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU) was up to 3 percent better for the best performing instance segmentation model, the average pixel accuracy and weighted IoU were slightly better for the best performing semantic segmentation model. Moreover, the inferences of the instance segmentation models are easier to implement for further processing pipelines in clinical decision support.

中文翻译：

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基于卷积神经网络的腰椎 X 射线自动分割和标记

本研究的目的是研究在 730 个手动注释的侧腰​​椎 X 射线上训练的不同分割网络的分割精度。实例分割网络与语义分割网络进行了比较。研究队列包括患病脊柱和金属植入物的术后图像。性能最佳的实例分割模型的平均平均准确度和平均交集 (IoU) 高出 3%，而性能最佳的语义分割模型的平均像素精度和加权 IoU 略好一些。此外，实例分割模型的推断更容易实现，以用于临床决策支持中的进一步处理管道。