LV-Net: Anatomy-aware lateral ventricle shape modeling with a case study on Alzheimer's disease
This work addresses shape variability and segmentation challenges in neurological disease biomarker analysis, specifically for Alzheimer's disease, but is incremental as it builds on existing template deformation methods.
The authors tackled the problem of lateral ventricle shape analysis from brain MRI by introducing LV-Net, a framework that produces individualized 3D meshes using an anatomy-aware template, achieving superior reconstruction accuracy and more reliable shape descriptors across datasets.
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.