LV-Net: Anatomy-aware lateral ventricle shape modeling with a case study on Alzheimer's disease

TL;DR

LV-Net is a new framework that accurately models lateral ventricle shapes from MRI scans, improving robustness and anatomical correspondence, and is applied to Alzheimer's disease analysis to identify disease-related subregions.

Contribution

The paper introduces LV-Net, a novel anatomy-aware deformation framework for 3D LV shape modeling that enhances accuracy and correspondence across subjects, especially under segmentation imperfections.

Findings

LV-Net achieves superior reconstruction accuracy.

LV-Net provides more reliable shape descriptors.

LV-Net identifies LV subregions associated with Alzheimer's.

Abstract

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.