Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation
This work addresses domain adaptation for cardiac ultrasound segmentation, offering potential clinical benefits by reducing reliance on labels, though it appears incremental in leveraging human gaze data.
The paper tackled the problem of domain adaptation for cardiac ultrasound image segmentation by introducing a gaze-assisted human-centric framework, which improved segmentation effectiveness in the target domain compared to GAN-based and self-train methods.
Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo-label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive guidance to help model adapt to target domain and reduce reliance on labels. Doctor gaze trajectories contains a large amount of cross-domain human guidance. To leverage gaze information and human cognition for guiding domain adaptation, we propose gaze-assisted human-centric domain adaptation (GAHCDA), which reliably guides the domain adaptation of cardiac ultrasound images. GAHCDA includes following modules: (1) Gaze Augment Alignment (GAA): GAA enables the model to obtain human cognition general features to recognize segmentation target in different domain of cardiac ultrasound images like humans. (2) Gaze Balance Loss (GBL): GBL fused gaze heatmap with outputs which makes the segmentation result structurally closer to the target domain. The experimental results illustrate that our proposed framework is able to segment cardiac ultrasound images more effectively in the target domain than GAN-based methods and other self-train based methods, showing great potential in clinical application.