High performance of deep learning on medical image segmentation rely on large-scale pixel-
level dense annotations, which poses a substantial burden on medical experts due to the …

Superpixels play a crucial role in image processing by partitioning an image into clusters of
pixels with similar visual attributes. This facilitates subsequent image processing tasks …

Despite that deep learning has achieved state-of-the-art performance for automatic medical
image segmentation, it often requires a large amount of pixel-level manual annotations for …

Deep learning-based 3D medical image segmentation typically demands extensive densely
labeled data. Yet, voxel-wise annotation is laborious and costly to obtain. Cross-annotation …

In weakly supervised medical image segmentation, the absence of structural priors and the
discreteness of class feature distribution present a challenge, ie, how to accurately …

Fully-supervised airway segmentation has accomplished significant triumphs over the years
in aiding pre-operative diagnosis and intra-operative navigation. However, full voxel-level …

Deep learning-based automated segmentation of vascular structures in preoperative CT
angiography (CTA) images contributes to computer-assisted diagnosis and interventions …

Scribble-based weakly supervised segmentation techniques offer comparable performance
to fully supervised methods while significantly reducing annotation costs, making them an …

Scribble supervision, a common form of weakly supervised learning, involves annotating
pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This …

Supervised deep learning crucially depends on large amount of high-quality annotation
data. While labeling for classification and grading tasks is rather efficient to achieve, labeling …