Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Abstract

Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods. Most existing semi-supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape-aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state-of-the-art approaches with improved shape estimation, which validates its efficacy. Code is available at https://github.com/kleinzcy/SASSnet.

Appear in MICCAI2020

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• Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
• Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
• Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
• 3D Brain and Heart Volume Generative Models: A Survey
• Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation
• Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
• Diversified Multi-prototype Representation for Semi-supervised Segmentation
• Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
• Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
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