Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
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.
