Beyond Strong labels: Weakly-supervised Learning Based on Gaussian
  Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in
  Non-contrast CTs
Qixiang Ma Antoine Łucas Huazhong Shu Adrien Kaladji Pascal Haigron
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.