用深度学习自动分割骨盆骨折碎片，提升术中定位精度
A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images
- 分三步：先分骨骼类别，再切碎骨折块，最后优化结果
- 骨折分割平均交并比达0.78，解剖结构达0.91
- 适合骨科影像分析与手术导航系统开发人员
骨盆骨折多由高能量创伤引起，常需手术治疗。通过CT和二维X射线成像进行图像配准，可将术前规划导入手术室，实现术中快速调整。其中，从二维X射线图像中分割骨盆骨折部位，有助于精确定位骨碎片，并指导螺钉或钢板的放置。本文提出一种基于深度学习的类别-碎片分割（CFS）框架，用于自动分割二维X射线图像中的骨盆骨碎片。该框架包含三个连续步骤：类别分割、碎片分割和后处理。最佳模型在解剖结构分割上达到0.91的交并比，在骨折分割上达到0.78。结果表明，CFS框架具有良好的有效性和准确性。
原文摘要 · Abstract (English)
Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging are used to transfer the surgical plan to the operating room through image registration, enabling quick intraoperative adjustments. Specifically, segmenting pelvic fractures from 2D X-ray imaging can assist in accurately positioning bone fragments and guiding the placement of screws or metal plates. In this study, we propose a novel deep learning-based category and fragment segmentation (CFS) framework for the automatic segmentation of pelvic bone fragments in 2D X-ray images. The framework consists of three consecutive steps: category segmentation, fragment segmentation, and post-processing. Our best model achieves an IoU of 0.91 for anatomical structures and 0.78 for fracture segmentation. Results demonstrate that the CFS framework is effective and accurate.
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