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Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation

arXiv 2502.03781 ↗First posted 6 Feb 2025cs.CV eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 6 Feb 2025.

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References · 25

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13 more
  1. 2021MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentationcited by 43
  2. 2019The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GANcited by 30
  3. 2021Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentationcited by 28
  4. 2021Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptationcited by 24
  5. 2022Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structurescited by 18
  6. 2020MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraintcited by 17
  7. 2021Early Myocardial Infarction Detection over Multi-view Echocardiographycited by 9
  8. 2023ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentationcited by 7
  9. 2022Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentationcited by 6
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  11. 2023Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptationcited by 5
  12. 2021Ultrasound Domain Adaptation Using Frequency Domain Analysiscited by 4
  13. 2022Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Studycited by 2

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