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Lateral Ventricle Shape Modeling using Peripheral Area Projection for Longitudinal Analysis

Wonjung Park, Suhyun Ahn, Jinah Park

TL;DR

Problem: LV shape deformation provides morphometric cues for brain atrophy, but traditional analyses do not guarantee correspondence of points to the same surrounding regions over time. Approach: a deep learning–based LV shape modeling framework with peripheral-area projection, segmenting LV and surrounding areas, assigning baseline and follow-up points to nearby regions, and deforming the follow-up mesh with a PointNet-based module guided by a composite loss $L_{dist}$ and $L_{reg}$, where $L_{dist} = \lambda_{cf} L_{cf} + \lambda_{pm} L_{pm} + \sum_{i=1}^{m} \lambda_{pm_i} L_{pm_i}$ and $L_{reg} = \lambda_{vert} L_{vert} + \lambda_{edge} L_{edge} + \lambda_{normal} L_{normal} + \lambda_{lap} L_{lap}$. Results: evaluated on the OASIS dataset (normal and demented, $n=10$ each) showing larger local deformations in demented subjects near several neighboring regions, with no significant sex differences ($p>0.26$). Significance: provides an interpretable, locality-aware framework for longitudinal LV analysis that accounts for surrounding brain structures, enabling more precise morphometric assessments of brain atrophy.

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.

Lateral Ventricle Shape Modeling using Peripheral Area Projection for Longitudinal Analysis

TL;DR

Problem: LV shape deformation provides morphometric cues for brain atrophy, but traditional analyses do not guarantee correspondence of points to the same surrounding regions over time. Approach: a deep learning–based LV shape modeling framework with peripheral-area projection, segmenting LV and surrounding areas, assigning baseline and follow-up points to nearby regions, and deforming the follow-up mesh with a PointNet-based module guided by a composite loss and , where and . Results: evaluated on the OASIS dataset (normal and demented, each) showing larger local deformations in demented subjects near several neighboring regions, with no significant sex differences (). Significance: provides an interpretable, locality-aware framework for longitudinal LV analysis that accounts for surrounding brain structures, enabling more precise morphometric assessments of brain atrophy.

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.

Paper Structure

This paper contains 5 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Method
  3. Results
  4. Conclusion
  5. Acknowledgements

Figures (2)

  • Figure 1: Overall architecture to estimate longitudinal shape deformation of lateral ventricle.
  • Figure 2: Comparison of longitudinal averaged vertex displacement in peripheral brain areas between two groups: normal (n=10) and demented (n=10) subjects. The displacement is normalized by the product of the time interval between two longitudinal MRIs (in year) and one-third power of the individual estimated total intracranial volume.