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.

翻译：肝脏血管分割是肝切除术前规划中的关键任务。现有的基于二维或三维卷积的先进方法主要针对二维CT横断面图像进行肝脏血管分割，未能充分考虑肝脏血管的全局拓扑结构。为保持这种全局血管拓扑，我们借鉴CT重建过程中的底层物理原理，并将其应用于肝脏血管分割。具体而言，我们提出了top-k最大强度投影的概念，该方法通过沿每个投影方向保留前k个最大值（而非传统沿投影方向的积分运算）来模拟CT重建过程。我们利用这些top-k最大投影作为条件输入扩散模型，以生成三维肝脏血管树。我们在3D-ircadb-01数据集上评估了所提出的三维肝脏血管分割方法，与现有研究相比，获得了最高的Dice系数、交并比（IoU）和灵敏度评分。