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Researchers develop multi-stage framework for bi-atrial segmentation from MRI

Researchers have developed a new multi-stage framework for segmenting bi-atrial regions from 3D MRI scans. The process begins with a preprocessing step using MCLAHE for contrast enhancement. Subsequently, a V-Net family model performs coarse segmentation on down-sampled images, followed by a second V-Net model for fine segmentation of the target region. The framework utilizes an asymmetric loss function to optimize model training. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This research introduces a novel approach to medical image segmentation, potentially improving diagnostic accuracy for cardiac conditions.

RANK_REASON The cluster contains an academic paper detailing a new segmentation framework.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Hao Wen, Jingsu Kang ·

    Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models

    arXiv:2604.26251v1 Announce Type: new Abstract: We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the human heart. The pipeline consists of a preprocessing step using multidimensional c…

  2. arXiv cs.CV TIER_1 · Jingsu Kang ·

    Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models

    We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the human heart. The pipeline consists of a preprocessing step using multidimensional contrast limited adaptive histogram equalization …