用CT重建原理增强血管拓扑，提升3D肝脏血管分割精度。
Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation
- 引入顶K最大强度投影模拟CT重建过程
- 在3D-ircadb-01上实现最高Dice、IoU与敏感度
- 适合需要精确血管树结构的术前规划场景
肝脏血管分割是肝切除术前规划的关键任务。现有基于2D或3D卷积的方法多聚焦于2D CT横断面，忽视了肝脏血管的整体拓扑结构。为保持全局血管连通性，本文利用CT重建中的物理原理，提出顶K最大强度投影（Top-K Maximum Intensity Projection），通过保留每条投影方向上的前K个最大值，替代传统积分操作，模拟真实重建过程。该投影作为先验信息，用于引导扩散模型生成完整的3D肝脏血管树。在3D-ircadb-01数据集上的实验表明，本方法在Dice系数、交并比（IoU）和敏感度三项指标上均优于已有工作。
原文摘要 · Abstract (English)
Liver-vessel segmentation is an essential task in the pre-operative planning of liver resection. State-of-the-art 2D or 3D convolution-based methods focusing on liver vessel segmentation on 2D CT cross-sectional views, which do not take into account the global liver-vessel topology. To maintain this global vessel topology, we rely on the underlying physics used in the CT reconstruction process, and apply this to liver-vessel segmentation. Concretely, we introduce the concept of top-k maximum intensity projections, which mimics the CT reconstruction by replacing the integral along each projection direction, with keeping the top-k maxima along each projection direction. We use these top-k maximum projections to condition a diffusion model and generate 3D liver-vessel trees. We evaluate our 3D liver-vessel segmentation on the 3D-ircadb-01 dataset, and achieve the highest Dice coefficient, intersection-over-union (IoU), and Sensitivity scores compared to prior work.
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