Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models
arXiv cs.AI / 4/30/2026
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Key Points
- The paper proposes a multi-stage pipeline for multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) cardiac MRI using V-Net family models.
- It first preprocesses the input with multidimensional contrast limited adaptive histogram equalization (MCLAHE) to improve image quality before segmentation.
- The approach performs coarse segmentation on MCLAHE-enhanced, down-sampled MRI using a V-Net variant, then refines the result with a second V-Net trained to produce fine segmentation from the coarse region.
- Training uses an asymmetric loss function to optimize model weights for the segmentation task.
- The work is published as an arXiv announcement (cross), indicating a research contribution rather than an deployed product or clinical release.
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