Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation

TL;DR

This paper introduces a hierarchical deep learning framework with an attention mechanism and semi-supervised training to improve vessel segmentation accuracy, especially in low-contrast regions, addressing annotation scarcity and reducing error propagation.

Contribution

It presents a novel hierarchical network with attention and uncertainty-aware semi-supervised learning for vessel segmentation, outperforming existing methods.

Findings

Achieves state-of-the-art results on retinal vessel segmentation.

Effectively localizes low-contrast capillary regions.

Reduces reliance on manual annotations.

Abstract

The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel bring a lot of ambiguity in vessel segmentation and sub-type recognition, especially for the low-contrast capillary regions. Furthermore, recent two-staged approaches would accumulate and even amplify these inaccuracies from the first-stage whole vessel segmentation into the second-stage sub-type vessel pixel-wise classification. Moreover, the scarcity of manual annotation in organ vessels poses another challenge. In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels. In addition, we propose an uncertainty-aware semi-supervised training framework to alleviate the annotation-hungry limitation of deep models. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images.