用解剖先验提升胰腺自动分割准确率
Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
- 引入解剖先验信息优化分割模型
- Dice分数提升6%，哈斯多夫距离减少36.5毫米
- 显著降低漏检率，适合医学影像分析场景
腹部CT中胰腺的精准分割对识别胰腺病变和提取影像生物标志物至关重要。以往研究主要聚焦于改进分割模型结构或使用预/后处理技术。本文探索解剖先验在胰腺分割中的作用：训练了两个3D全分辨率nnU-Net模型，一个基于公开PANORAMA数据集的8个细化标签，另一个结合了由公开TotalSegmentator工具生成的标签。加入解剖先验后，胰腺分割的Dice分数提升6%（p < .001），哈斯多夫距离减少36.5毫米（p < .001）。此外，使用解剖先验时胰腺始终被检测到，而未使用时有8次漏检。结果表明，解剖先验有助于提升胰腺分割性能及后续影像生物标志物提取。
原文摘要 · Abstract (English)
An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation has primarily focused on modifying the segmentation model architecture or utilizing pre- and post-processing techniques. In this article, we investigate the utility of anatomical priors to enhance the segmentation performance of the pancreas. Two 3D full-resolution nnU-Net models were trained, one with 8 refined labels from the public PANORAMA dataset, and another that combined them with labels derived from the public TotalSegmentator (TS) tool. The addition of anatomical priors resulted in a 6\% increase in Dice score ($p < .001$) and a 36.5 mm decrease in Hausdorff distance for pancreas segmentation ($p < .001$). Moreover, the pancreas was always detected when anatomy priors were used, whereas there were 8 instances of failed detections without their use. The use of anatomy priors shows promise for pancreas segmentation and subsequent derivation of imaging biomarkers.
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