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

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.

References in corpus (8)

• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
• Temporal Ensembling for Semi-Supervised Learning
• Adversarial Learning for Semi-Supervised Semantic Segmentation
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
• 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
• Automatic Liver Lesion Detection using Cascaded Deep Residual Networks
• Leveraging Uncertainty Estimates for Predicting Segmentation Quality