Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

Area of Science:

• Medical image analysis
• Machine learning for healthcare
• Computer-assisted diagnosis

Background:

• Convolutional Neural Networks (CNNs) excel at medical image segmentation but require extensive manual annotations.
• Semi-Supervised Learning (SSL) reduces annotation needs but struggles with small datasets and cross-domain challenges.
• Leveraging external datasets is promising but hindered by domain shifts across modalities and organs.

Purpose of the Study:

• To develop a method for accurate medical image segmentation in a target domain using limited annotations.
• To address the cross-anatomy domain shift problem by adapting models trained on source domain data.
• To improve the performance of segmentation models when annotated data is scarce.

Main Methods:

• Proposes Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA).
• Employs Domain-Specific Batch Normalization (DSBN) for domain-specific feature normalization.
• Utilizes a cross-domain contrastive learning strategy for domain-invariant feature extraction.
• Integrates these into a Self-Ensembling Mean-Teacher (SE-MT) framework for exploiting unlabeled data.

Main Results:

• CS-CADA successfully adapts models to new anatomical domains with minimal target annotations.
• Demonstrates accurate segmentation of coronary arteries from X-ray images using retinal vessel data.
• Achieves precise segmentation of cardiac structures in MR images using fundus images as a source.
• Outperforms existing methods in challenging cross-anatomy domain adaptation scenarios.

Conclusions:

• CS-CADA effectively overcomes cross-anatomy domain shifts in medical image segmentation.
• The method significantly reduces the need for manual annotations in target domains.
• CS-CADA offers a viable solution for improving segmentation accuracy with limited data, leveraging diverse source datasets.