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Exploring Contrastive Pre-Training for Domain Connections in Medical Image Segmentation.
IEEE Transactions on Medical Imaging
|March 3, 2025
Summary
MedCon enhances medical image segmentation by using unsupervised contrastive pre-training to bridge domain gaps. This framework effectively handles diverse domain shifts, improving model generalization without complex adjustments.
Area of Science:
- Medical Imaging
- Computer Vision
- Machine Learning
Background:
- Unsupervised domain adaptation (UDA) in medical imaging seeks to generalize deep learning models across different data sources.
- Existing UDA methods often have complex designs, limited flexibility, and struggle with diverse clinical domain shifts.
- There is a need for robust UDA frameworks that leverage unlabeled data and handle varied domain inconsistencies.
Purpose of the Study:
- To introduce MedCon, a unified framework for unsupervised domain adaptation in medical image segmentation.
- To address limitations of existing UDA methods, including cumbersome designs and poor generalization across diverse clinical scenarios.
- To leverage unsupervised contrastive pre-training for effective domain connection and handling of varied domain shifts.
Main Methods:
- Employs general unsupervised contrastive pre-training on unlabeled images to establish domain connections.
- Utilizes a shared-weight encoder-decoder architecture for generating pixel-level representations.
- Constructs positive-negative pairs from local and global scales to capture intra- and inter-domain connections of anatomical structures.
- Fine-tunes the pre-trained backbone using source-domain images for per-pixel semantic segmentation.
Main Results:
- MedCon effectively manages a wide range of domain shifts in medical image segmentation.
- The framework demonstrates superior generalization capabilities compared to previous UDA methods.
- Experiments on diverse medical image datasets validate the effectiveness of MedCon.
Conclusions:
- MedCon provides a unified and effective solution for unsupervised domain adaptation in medical image segmentation.
- The proposed contrastive pre-training approach successfully establishes domain connections and handles diverse shifts.
- MedCon offers improved generalization and performance, outperforming existing methods on various datasets.
