Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of 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 novel weakly-supervised framework using the elliptical topology nature of vascular structures in CT slices. It includes an efficient annotation process based on our proposed standards, an approach of generating 2D Gaussian heatmaps serving as pseudo labels, and 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 with a reduction of 68% of the Hausdorff distance for 3D model.

中文翻译：

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超越强标签：基于高斯伪标签的弱监督学习，用于非对比 CT 中椭圆状血管结构的分割


术前 CT 血管造影 （CTA） 图像中基于深度学习的血管结构自动分割有助于计算机辅助诊断和干预。虽然 CTA 是通用标准，但非增强 CT 成像具有避免与造影剂相关的并发症的优势。然而，由于血管边界的模糊性，劳动密集型标记和高标记可变性的挑战阻碍了非对比 CT 中传统的基于强标记的完全监督学习。本文利用 CT 切片中血管结构的椭圆拓扑性质介绍了一种新的弱监督框架。它包括一个基于我们提出的标准的高效注释过程，一种生成 2D 高斯热图作为伪标签的方法，以及一个通过体素重建损失和分布损失与伪标签相结合的训练过程。我们在一个包含非对比 CT 扫描的本地数据集和两个公共数据集上评估了所提出的方法的有效性，特别是针对腹主动脉。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（平均占 Dice 分数的 1.54%），将标记时间缩短了约 82.0%。生成伪标签的效率允许在训练集中包含与标签无关的外部数据，从而进一步提高性能（平均为 Dice 分数的 2.74%），减少 66.3% 的标记时间，其中标记时间仍然大大少于强标签。在公共数据集上，伪标签使 2D 模型的 Dice 分数总体提高了 1.95%，而 3D 模型的 Hausdorff 距离减少了 68%。