基于3D心脏MRI的双心房分割，分阶段提升精度
Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models
- 多阶段流程：先粗分割后精分割，结合MCLAHE增强图像
- 使用V-Net家族模型，采用非对称损失优化权重
- 适用于心脏影像分析，尤其适合心房结构精细分割
我们提出一种用于从3D晚期钆增强（LGE）MRI中进行多类别双心房分割的多阶段框架。该流程包括：使用多维对比度受限自适应直方图均衡化（MCLAHE）进行预处理；利用下采样后的MCLAHE增强MRI，通过V-Net家族模型完成粗略区域分割；再通过另一个V-Net模型对粗分割区域进行精细化分割。采用非对称损失函数优化模型权重，以应对心房结构在图像中占比小的问题。
原文摘要 · Abstract (English)
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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