mlsanityData to 14 Oct 2026

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

arXiv 2309.11145 ↗First posted 20 Sep 2023cs.CVcited by 22

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Abstract (arXiv)

Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains. Existing UDA segmentation methods are not suitable for this task because they do not model local information and the cyclical consistency of heartbeat. In this paper, we introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for cardiac structure segmentation. Our GraphEcho comprises two innovative modules, the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle Consistency (TCC) module, which utilize prior knowledge of echocardiogram videos, i.e., consistent cardiac structure across patients and centers and the heartbeat cyclical consistency, respectively. These two modules can better align global and local features from source and target domains, improving UDA segmentation results. Experimental results showed that our GraphEcho outperforms existing state-of-the-art UDA segmentation methods. Our collected dataset and code will be publicly released upon acceptance. This work will lay a new and solid cornerstone for cardiac structure segmentation from echocardiogram videos. Code and dataset are available at: https://github.com/xmed-lab/GraphEcho

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 36 of its 49 references that are in the map. First posted on arXiv 20 Sep 2023.

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36 of 49 references.

References · 36

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24 more
  1. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 244
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 7.

22 papers.

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  1. 2024UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasetscited by 24
  2. 2025USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencodingcited by 16
  3. 2024From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learningcited by 15
  4. 2024CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videoscited by 11
  5. 2025DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?cited by 10
  6. 2025Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentationcited by 9
  7. 2024Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Codingcited by 5
  8. 2025DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networkscited by 3
  9. 2026US-JEPA: A Joint Embedding Predictive Architecture for Ultrasoundcited by 2
  10. 2024MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentationcited by 1
  11. 2025Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videoscited by 1
  12. 2025Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMscited by 1
10 more
  1. 2026UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentationcited by 1
  2. 2026UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasoundcited by 1
  3. 2025MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentationcited by 0
  4. 2025CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Mannercited by 0
  5. 2025MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generationcited by 0
  6. 2025InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiographycited by 0
  7. 2026Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentationcited by 0
  8. 2026Anatomical Prior-Driven Framework for Autonomous Robotic Cardiac Ultrasound Standard View Acquisitioncited by 0
  9. 2026EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Functioncited by 0
  10. 2026UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restorationcited by 0