用解剖先验构建脑室3D模型，提升阿尔茨海默病诊断精度
LV-Net: Anatomy-aware lateral ventricle shape modeling with a case study on Alzheimer's disease
- 基于联合脑室-海马模板，通过形变生成个体化3D脑室网格
- 在不同数据集上重建精度优于现有方法，对分割误差不敏感
- 可定位与阿尔茨海默病显著相关的脑室亚区，适合神经影像研究
侧脑室（LV）形状分析有望成为神经疾病生物标志物，但受个体间形状差异大及磁共振成像分辨率有限导致的分割困难制约。我们提出LV-Net，一种从脑部MRI生成个体化3D LV网格的新框架，通过形变一个包含解剖先验的联合脑室-海马模板网格实现。通过嵌入解剖关系，该方法减少边界分割伪影并增强重建鲁棒性。同时，基于解剖邻接性对模板网格顶点进行分类，提升了跨被试点对应精度，从而获得更准确的脑室形状统计。实验表明，即使存在分割缺陷，LV-Net仍能实现更优的重建精度，并在多种数据集上提供更可靠的形状描述符。最后，我们将该方法应用于阿尔茨海默病分析，识别出与认知正常对照组显著相关的脑室亚区。代码已开源：https://github.com/PWonjung/LV_Shape_Modeling。
原文摘要 · Abstract (English)
Lateral ventricle (LV) shape analysis holds promise as a biomarker for neurological diseases; however, challenges remain due to substantial shape variability across individuals and segmentation difficulties arising from limited MRI resolution. We introduce LV-Net, a novel framework for producing individualized 3D LV meshes from brain MRI by deforming an anatomy-aware joint LV-hippocampus template mesh. By incorporating anatomical relationships embedded within the joint template, LV-Net reduces boundary segmentation artifacts and improves reconstruction robustness. In addition, by classifying the vertices of the template mesh based on their anatomical adjacency, our method enhances point correspondence across subjects, leading to more accurate LV shape statistics. We demonstrate that LV-Net achieves superior reconstruction accuracy, even in the presence of segmentation imperfections, and delivers more reliable shape descriptors across diverse datasets. Finally, we apply LV-Net to Alzheimer's disease analysis, identifying LV subregions that show significantly associations with the disease relative to cognitively normal controls. The codes for LV shape modeling are available at https://github.com/PWonjung/LV_Shape_Modeling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。