mlsanityData to 14 Oct 2026

Echo-DND: A dual noise diffusion model for robust and precise left ventricle segmentation in echocardiography

arXiv 2506.15166 ↗First posted 18 Jun 2025cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Recent advancements in diffusion probabilistic models (DPMs) have revolutionized image processing, demonstrating significant potential in medical applications. Accurate segmentation of the left ventricle (LV) in echocardiograms is crucial for diagnostic procedures and necessary treatments. However, ultrasound images are notoriously noisy with low contrast and ambiguous LV boundaries, thereby complicating the segmentation process. To address these challenges, this paper introduces Echo-DND, a novel dual-noise diffusion model specifically designed for this task. Echo-DND leverages a unique combination of Gaussian and Bernoulli noises. It also incorporates a multi-scale fusion conditioning module to improve segmentation precision. Furthermore, it utilizes spatial coherence calibration to maintain spatial integrity in segmentation masks. The model's performance was rigorously validated on the CAMUS and EchoNet-Dynamic datasets. Extensive evaluations demonstrate that the proposed framework outperforms existing SOTA models. It achieves high Dice scores of 0.962 and 0.939 on these datasets, respectively. The proposed Echo-DND model establishes a new standard in echocardiogram segmentation, and its architecture holds promise for broader applicability in other medical imaging tasks, potentially improving diagnostic accuracy across various medical domains. Project page: https://abdur75648.github.io/Echo-DND

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 26 of its 54 references that are in the map. First posted on arXiv 18 Jun 2025.

Builds on

26 of 54 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation10 Apr
  2. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
  3. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec
  4. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 22

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10 more
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  2. 2023Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
  3. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
  4. 2023Echocardiography video synthesis from end diastolic semantic map via diffusion model
  5. 2024Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis
  6. 2024P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation
  7. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
  8. 2024EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data