Partial Unbalanced Feature Transport for Cross-Modality Cardiac Image Segmentation.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Deep learning excels at cardiac image segmentation but struggles with domain shift, limiting performance across different image sources.
• Unsupervised Domain Adaptation (UDA) aims to bridge this gap by aligning features from labeled source domains to unlabeled target domains.
• Existing UDA methods often overlook crucial structural information and can suffer from inference bias.

Purpose of the Study:

• To propose a novel framework, Partial Unbalanced Feature Transport (PUFT), for cross-modality cardiac image segmentation.
• To enhance UDA by integrating Continuous Normalizing Flow-based Variational Auto-Encoders (CNF-VAE) and Partial Unbalanced Optimal Transport (PUOT).
• To address limitations in previous UDA approaches by improving posterior estimation and incorporating structural information.

Main Methods:

• Developed PUFT, a framework utilizing two CNF-VAEs to model latent feature distributions.
• Employed PUOT to minimize domain discrepancy, leveraging source domain labels to constrain the transport plan.
• Incorporated structural information extraction within the PUOT strategy to better align features.

Main Results:

• PUFT demonstrated superior performance in cross-modality cardiac image segmentation compared to state-of-the-art methods.
• The integration of CNF-VAE alleviated inference bias by providing more accurate posterior estimations.
• PUOT effectively utilized label and structural information to reduce domain shift.

Conclusions:

• The proposed PUFT framework offers a significant advancement in unsupervised domain adaptation for medical image segmentation.
• CNF-VAE and PUOT integration effectively mitigates domain shift, leading to improved segmentation accuracy.
• PUFT shows promise for robust cardiac image segmentation across diverse imaging modalities and datasets.