Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. The practical adoption of fully supervised segmentation models is impeded by the intricacy and time-consuming nature of annotating vessels in the 3D space. This has spurred the exploration of weakly-supervised approaches that reduce reliance on expensive segmentation annotations. Despite this, existing weakly supervised methods employed in organ segmentation, which encompass points, bounding boxes, or graffiti, have exhibited suboptimal performance when handling sparse vascular structure. To alleviate this issue, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation, and the 2D labels are utilized to provide guidance and oversight for training 3D vessel segmentation model. Initially, we generate pseudo-labels for 3D blood vessels using the annotations of 2D projections. Subsequently, taking into account the acquisition method of the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep features via MIP to further improve segmentation performance. Furthermore, we integrate confidence learning and uncertainty estimation to refine the generated pseudo-labels, followed by fine-tuning the segmentation network. Our method is validated on five datasets (including cerebral vessel, aorta and coronary artery), demonstrating highly competitive performance in segmenting vessels and the potential to significantly reduce the time and effort required for vessel annotation. Our code is available at: https://github.com/gzq17/Weakly-Supervised-by-MIP.

翻译：血管结构分割在医学分析和临床应用中至关重要。全监督分割模型在实际应用中的推广受到三维空间内标注血管的复杂性和耗时的阻碍。这促使研究人员探索弱监督方法，以减少对昂贵分割标注的依赖。尽管如此，现有用于器官分割的弱监督方法（包括点、边界框或涂鸦标注）在处理稀疏血管结构时表现欠佳。为解决这一问题，我们采用最大强度投影（MIP）将三维体数据降维至二维图像以实现高效标注，并利用二维标签为训练三维血管分割模型提供指导和监督。首先，我们利用二维投影的标注生成三维血管的伪标签。随后，考虑二维标签的获取方式，我们引入一个通过MIP融合二维-三维深度特征的弱监督网络，以进一步提升分割性能。此外，我们整合置信度学习与不确定性估计来优化生成的伪标签，并微调分割网络。该方法在五个数据集（包括脑部血管、主动脉和冠状动脉）上进行了验证，在血管分割任务中展现出极具竞争力的性能，并展现出显著减少血管标注所需时间和精力的潜力。我们的代码已开源：https://github.com/gzq17/Weakly-Supervised-by-MIP。