Delineating 3D blood vessels is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Supervised deep learning has demonstrated its superior capacity in automatic 3D vessel segmentation. However, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse hinder the clinical applications of supervised models. To avoid the repetitive and laborious annotating and make full use of existing vascular annotations, this paper proposes a novel 3D shape-guided local discrimination model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images; secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop-and-Overlap strategy is presented to generate reference masks from 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the tree-shaped morphology of 3D vessels; thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly. To further enhance the model's robustness and reliability, the orientation-invariant CNN module and Reliability-Refinement algorithm are presented. Experimental results from the public 3D cerebrovascular and 3D arterial tree datasets demonstrate that our model achieves comparable effectiveness against nine supervised models.

翻译：三维血管的勾画对临床诊断与治疗至关重要，但由于结构复杂多变及成像条件差异，这一任务具有挑战性。监督式深度学习已在自动三维血管分割中展现出卓越能力，然而，其对昂贵的三维手动标注的依赖以及标注复用能力的局限，制约了监督模型在临床中的应用。为避免重复繁琐的标注工作并充分利用现有血管标注信息，本文提出一种新颖的三维形状引导局部判别模型，在公共2D血管标注的有限引导下实现三维血管分割。核心假设是：三维血管由语义相似的体素构成，并呈现树状形态。据此，本文首先提出三维区域判别损失，用于学习衡量体素间相似度的判别性表征，并聚类语义一致的体素以形成未标注图像中的候选三维血管分割；其次，基于二维与三维血管树状形态的相似性，提出裁剪-重叠策略以从二维结构不可知血管标注生成适配多种血管结构的参考掩膜，并引入对抗性损失引导三维血管的树状形态；第三，提出时间一致性损失以促进训练稳定性并确保模型平稳更新。为进一步增强模型鲁棒性与可靠性，还提出了方向不变卷积模块和可靠性精炼算法。在公开三维脑血管与三维动脉树数据集上的实验结果表明，本模型在性能上可与九种监督模型相媲美。