Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of manual 3D annotations from experts, which are laborious to obtain. This is especially the case for 3D vessel segmentation, as vessels are sparse yet spread out over many slices and disconnected when visualized in 2D slices. In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision. We perform extensive experiments on the segmentation of peripancreatic arteries on 3D contrast-enhanced CT images and demonstrate how well we capture the rich depth information from 2D projections. We demonstrate that by annotating a single, randomly chosen projection for each training sample, we obtain comparable performance to annotating multiple 2D projections, thereby reducing the annotation effort. Furthermore, by mapping the 2D labels to the 3D space using depth information and incorporating this into training, we almost close the performance gap between 3D supervision and 2D supervision. Our code is available at: https://github.com/alinafdima/3Dseg-mip-depth.

翻译：三维血管自动分割是许多血管疾病定量诊断与治疗的关键步骤。现有研究多采用深度学习方法对三维血管分割进行探索，但训练三维深度网络需要大量专家手工标注的三维数据，获取成本高昂。对于三维血管分割尤为如此——血管结构稀疏但分布广泛于多个切片，且在二维切片中呈现不连续状态。本文提出一种创新方法，仅需对每张训练图像标注一个二维投影，结合深度监督即可实现三维胰周动脉分割。我们在三维对比增强CT图像上进行胰周动脉分割的广泛实验，验证了该方法从二维投影中有效提取丰富深度信息的能力。实验表明，仅需为每个训练样本标注一个随机选择的二维投影，即可获得与标注多个二维投影相当的性能，从而显著降低标注工作量。此外，通过利用深度信息将二维标签映射至三维空间并融入训练过程，我们几乎弥合了三维监督与二维监督之间的性能差距。代码已开源：https://github.com/alinafdima/3Dseg-mip-depth