Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Imaging

Background:

• Unsupervised domain adaptation (UDA) is crucial for left ventricle (LV) segmentation in echocardiography to ensure clinical utility across various devices and institutions.
• Challenges in UDA for echocardiography include anatomical context shifts and noise interference, hindering robust segmentation performance.
• Existing UDA methods struggle to address these domain-specific challenges effectively.

Purpose of the Study:

• To introduce a novel anatomy-guided spatio-temporal affinity framework for unsupervised domain adaptation in left ventricle segmentation.
• To address anatomical context shift and noise interference in echocardiographic images.
• To improve the generalizability and clinical applicability of LV segmentation models.

Main Methods:

• Developed an anatomy-guided spatio-temporal affinity framework comprising Anatomical Context Alignment (ACA) and Anatomical Affinity Refinement (AAR) modules.
• ACA module adapts source domain to target domain's anatomical context using LV-dominant cropping and 4C-complete mirroring.
• AAR module enforces fine-grained anatomical consistency and noise suppression via spatial (SAR) and temporal (TAR) affinity modeling.

Main Results:

• Demonstrated that anatomical context shift is a primary driver of domain discrepancy in echocardiographic LV segmentation.
• The proposed framework effectively alleviates anatomical context shift and significantly suppresses noise interference.
• Achieved superior performance compared to state-of-the-art UDA methods on three public datasets (CAMUS, EchoNet-Dynamic, CardiacUDA).

Conclusions:

• The anatomy-guided spatio-temporal affinity framework successfully enhances unsupervised domain adaptation for left ventricle segmentation.
• The method's ability to align anatomical context and refine features leads to improved segmentation accuracy and robustness.
• This work offers a promising solution for deploying reliable echocardiographic LV segmentation in diverse clinical settings.