Lateral Ventricle Shape Modeling using Peripheral Area Projection for Longitudinal Analysis

TL;DR

This paper introduces a novel deep learning method for analyzing lateral ventricle shape changes by considering surrounding brain areas, aiding in understanding disease-related morphometric alterations.

Contribution

It presents the first approach to incorporate surrounding brain areas into LV shape analysis using peripheral area projection and shape deformation matching.

Findings

Significant differences in surrounding area projections between normal and demented subjects.

The method effectively captures local LV deformation related to disease.

Demonstrated potential for improved longitudinal brain analysis.

Abstract

The deformation of the lateral ventricle (LV) shape is widely studied to identify specific morphometric changes associated with diseases. Since LV enlargement is considered a relative change due to brain atrophy, local longitudinal LV deformation can indicate deformation in adjacent brain areas. However, conventional methods for LV shape analysis focus on modeling the solely segmented LV mask. In this work, we propose a novel deep learning-based approach using peripheral area projection, which is the first attempt to analyze LV considering its surrounding areas. Our approach matches the baseline LV mesh by deforming the shape of follow-up LVs, while optimizing the corresponding points of the same adjacent brain area between the baseline and follow-up LVs. Furthermore, we quantitatively evaluated the deformation of the left LV in normal (n=10) and demented subjects (n=10), and we found that each surrounding area (thalamus, caudate, hippocampus, amygdala, and right LV) projected onto the surface of LV shows noticeable differences between normal and demented subjects.