超越强标签：基于高斯伪标签的弱监督学习用于非增强CT中类椭圆形血管结构的分割
Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs
AI总结：
针对非增强CT血管分割标注难的问题，提出基于椭圆拓扑的弱监督框架，用高斯伪标签训练，在多数据集上性能优于全监督且大幅减少标注时间。
摘要：
基于深度学习的术前CT扫描血管结构自动分割有助于血管疾病的计算机辅助诊断与介入手术。CT血管造影（CTA）是通用标准，而非增强CT成像作为无造影剂风险的替代方案具有重要意义，可避免与造影剂相关的并发症。然而，血管边界模糊导致的标注工作量大、标注变异性高的挑战，阻碍了传统基于强标签的全监督学习在非增强CT中的应用。本文提出一种利用切片中椭圆拓扑结构的弱监督框架，包括：1）基于预定义标准的高效标注流程；2）椭圆拟合处理；3）生成作为伪标签的二维高斯热图；4）结合体素重建损失与伪标签分布损失的训练过程。我们在包含非增强CT扫描的1个本地数据集和2个公开数据集上评估了所提方法的有效性，重点关注腹主动脉。在本地数据集上，我们基于伪标签的弱监督学习方法优于基于强标签的全监督学习（Dice系数平均提升1.54%），标注时间减少约82.0%。伪标签生成的高效性使得训练集中可纳入无标签外部数据，从而进一步提升性能（Dice系数平均提升2.74%），同时标注时间减少66.3%，且标注时间仍远少于强标签的标注时间。在公开数据集上，伪标签使二维模型的Dice系数整体提升1.95%，三维模型的豪斯多夫距离减少11.65个体素间距。
英文摘要：
Deep-learning-based automated segmentation of vascular structures in preoperative CT scans contributes to computer-assisted diagnosis and intervention procedure in vascular diseases. While CT angiography (CTA) is the common standard, non-contrast CT imaging is significant as a contrast-risk-free alternative, avoiding complications associated with contrast agents. However, the challenges of labor-intensive labeling and high labeling variability due to the ambiguity of vascular boundaries hinder conventional strong-label-based, fully-supervised learning in non-contrast CTs. This paper introduces a weakly-supervised framework using ellipses' topology in slices, including 1) an efficient annotation process based on predefined standards, 2) ellipse-fitting processing, 3) the generation of 2D Gaussian heatmaps serving as pseudo labels, 4) a training process through a combination of voxel reconstruction loss and distribution loss with the pseudo labels. We assess the effectiveness of the proposed method on one local and two public datasets comprising non-contrast CT scans, particularly focusing on the abdominal aorta. On the local dataset, our weakly-supervised learning approach based on pseudo labels outperforms strong-label-based fully-supervised learning (1.54\% of Dice score on average), reducing labeling time by around 82.0\%. The efficiency in generating pseudo labels allows the inclusion of label-agnostic external data in the training set, leading to an additional improvement in performance (2.74\% of Dice score on average) with a reduction of 66.3\% labeling time, where the labeling time remains considerably less than that of strong labels. On the public dataset, the pseudo labels achieve an overall improvement of 1.95\% in Dice score for 2D models while a reduction of 11.65 voxel spacing in Hausdorff distance for 3D model.
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