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

TL;DR

Echo-DND introduces a dual-noise diffusion model with multi-scale fusion and spatial calibration to enhance left ventricle segmentation accuracy in noisy echocardiograms, outperforming existing methods on key datasets.

Contribution

The paper presents a novel dual-noise diffusion model with multi-scale fusion and spatial coherence calibration for improved LV segmentation in echocardiography.

Findings

Achieved Dice scores of 0.962 and 0.939 on CAMUS and EchoNet-Dynamic datasets.

Outperforms state-of-the-art models in LV segmentation accuracy.

Demonstrates robustness in noisy ultrasound images.

Abstract

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