Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

• URL: http://arxiv.org/abs/2105.07059v1
• Date: Fri, 14 May 2021 20:27:23 GMT
• Title: Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
• Authors: Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan
• Abstract summary: We present a novel Contrastive Voxel-wise Representation (CVRL) method with geometric constraints to learn global-local visual representations for medical image segmentation. Our framework can effectively learn global and local features by capturing 3D spatial context and rich anatomical information.
• Score: 2.3322477552758234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, manually annotating medical data is often laborious, and most existing learning-based approaches fail to accurately delineate object boundaries without effective geometric constraints. Contrastive learning, a sub-area of self-supervised learning, has recently been noted as a promising direction in multiple application fields. In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method with geometric constraints to learn global-local visual representations for volumetric medical image segmentation with limited annotations. Our framework can effectively learn global and local features by capturing 3D spatial context and rich anatomical information. Specifically, we introduce a voxel-to-volume contrastive algorithm to learn global information from 3D images, and propose to perform local voxel-to-voxel contrast to explicitly make use of local cues in the embedding space. Moreover, we integrate an elastic interaction-based active contour model as a geometric regularization term to enable fast and reliable object delineations in an end-to-end learning manner. Results on the Atrial Segmentation Challenge dataset demonstrate superiority of our proposed scheme, especially in a setting with a very limited number of annotated data.

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