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.

基于深度学习的自动化血管结构分割为血管疾病的计算机辅助诊断和介入手术提供有益的预防性 CT 扫描，该文章引入了一种基于弱监督框架，通过椭圆拟合和伪标签生成的方法取得了较好的效果。

基于高斯伪标签的非对比度 CT 中类椭圆血管结构分割的弱监督学习超越强标签

Beyond Strong labels: Weakly-supervised Learning Based on Gaussian  Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in  Non-contrast CTs