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ARXIV:2604.26251 · MEDICAL AI · SUBMITTED 30 APR · 15:14 UTC · FRESHNESS STALE
ARXIV:2604.26251MEDICAL AISUBMITTED 30 APR · 15:14 UTCFRESHNESS STALEHao Wen · Jingsu Kang · arXiv
A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement.
Opportunity summary
Pain A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement. The pipeline consists of a preprocessing step using multidimensional contrast…
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…
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Asymmetric loss is adopted to optimize the model weights.
Medical AI moved forward this cycle; last verified April 2026. Public score 0.0/10.
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A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement.
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10.48550/arXiv.2604.26251A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement.
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 contrast limited adaptive histogram equalization (MCLAHE); coarse region segmentation from MCLAHE-enhanced and down-sampled MRI using a V-Net family model; and fine segmentation from the coarse region using another V-Net model. Asymmetric loss is adopted to optimize the model weights.
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PROBLEM
A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement. The pipeline consists of a preprocessing step using multidimensional contrast limited adaptive histogram equaliz...
METHOD
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...
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Asymmetric loss is adopted to optimize the model weights.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 0.0/10.
{"file name": "input.pdf", "number of pages": 6, "author": "Hao Wen; Jingsu Kang", "title": "Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models"
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A multi-stage framework for segmenting bi-atrial regions in 3D cardiac MRI using V-Net models and asymmetric loss, with a preprocessing step for contrast enhancement.
Segment
Medical AI
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