无需手动标注血管结构，用图神经网络自动学习肝脏解剖先验知识。
Skip priors and add graph-based anatomical information, for point-based Couinaud segmentation
- 在点云上添加图推理模块，隐式学习血管拓扑关系。
- 在MSD和LiTS数据集上Dice分数优于四种基准方法。
- 适合需要高效肝段分割的临床术前规划场景。
肝癌术前规划依赖于从计算机断层扫描（CT）图像中进行Couinaud分段，以降低出血风险并指导切除操作。相较于体素化三维CT，基于点的表示能更好保留CT的物理分辨率。然而，点表示需依赖肝脏血管结构的先验知识，而手动获取该信息耗时费力。本文提出一种无需显式提供肝脏血管结构的点基耦纳德分段方法。通过在点特征上增加图推理模块，模型可自主学习点邻域间的关联性，从而隐式获取解剖学先验信息。该方法在MSD和LiTS公开数据集上的Dice系数与平均表面距离指标表现优异，超越四种前沿点基方法。代码已开源：https://github.com/ZhangXiaotong015/GrPn。
原文摘要 · Abstract (English)
The preoperative planning of liver surgery relies on Couinaud segmentation from computed tomography (CT) images, to reduce the risk of bleeding and guide the resection procedure. Using 3D point-based representations, rather than voxelizing the CT volume, has the benefit of preserving the physical resolution of the CT. However, point-based representations need prior knowledge of the liver vessel structure, which is time consuming to acquire. Here, we propose a point-based method for Couinaud segmentation, without explicitly providing the prior liver vessel structure. To allow the model to learn this anatomical liver vessel structure, we add a graph reasoning module on top of the point features. This adds implicit anatomical information to the model, by learning affinities across point neighborhoods. Our method is competitive on the MSD and LiTS public datasets in Dice coefficient and average surface distance scores compared to four pioneering point-based methods. Our code is available at https://github.com/ZhangXiaotong015/GrPn.
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