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Constraint-Based Unsupervised Domain Adaptation Network for Multi-Modality Cardiac Image Segmentation.

Xiuquan Du, Yueguo Liu

IEEE Journal of Biomedical and Health Informatics

|November 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel unsupervised domain adaptation network for cardiac image segmentation. The method improves segmentation accuracy on unannotated cardiac datasets, aiding clinical evaluation.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Cardiac CT and MRI images are crucial for heart function analysis.
Automatic cardiac image segmentation is challenging due to variations in shape and imaging techniques.

Purpose of the Study:

To propose a novel constraint-based unsupervised domain adaptation network for automatic cardiac image segmentation.
To address the challenges of domain shift in cardiac imaging datasets.

Main Methods:

Developed a network for mutual image translation between domains to ensure domain invariance.
Implemented cross-domain self-supervised learning with a novel loss function for accurate pseudo-label generation.
Utilized a multi-level aggregation segmentation network for refined target domain information.

Main Results:

Achieved 82.9% Dice score and 5.5 Average Symmetric Surface Distance (ASSD) on a public whole heart image segmentation dataset.
Demonstrated the effectiveness of the proposed method in segmenting unannotated cardiac datasets.

Conclusions:

The proposed unsupervised domain adaptation network significantly enhances automatic cardiac image segmentation.
This method offers valuable assistance for the clinical evaluation of unannotated cardiac imaging data.