用分割模型辅助主动脉瓣置换术前规划，提升解剖结构识别精度。
Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement
- 基于CT扫描构建细粒度伪标签，指导模型定位关键解剖结构。
- 改进损失函数后，分割Dice分数提升1.27%。
- 成果开源，适合心血管手术AI辅助研究者使用。
在基于医学影像的术前规划中，人工智能可辅助医生评估。本文针对经导管主动脉瓣置换术（TAVR）的术前规划指南，识别可通过语义分割模型支持的任务，使计算机断层扫描中的相关解剖结构可量化。首先，从粗粒度解剖信息生成细粒度的TAVR相关伪标签，用于训练分割模型，并评估其在扫描中识别结构的能力。此外，我们提出一种损失函数改进方法，在训练中实现+1.27%的Dice分数提升。所构建的细粒度伪标签与CT扫描数据已公开于https://doi.org/10.5281/zenodo.16274176。
原文摘要 · Abstract (English)
When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, we consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks, that may be supported via semantic segmentation models by making relevant anatomical structures measurable in computed tomography scans. We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans. Furthermore, we propose an adaptation to the loss function in training these segmentation models and through this achieve a +1.27% Dice increase in performance. Our fine-grained TAVR-relevant pseudo-labels and the computed tomography scans we build upon are available at https://doi.org/10.5281/zenodo.16274176.
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