通过周边区域投影分析侧脑室形状变化，揭示痴呆症早期脑萎缩特征。
Lateral Ventricle Shape Modeling using Peripheral Area Projection for Longitudinal Analysis
- 引入周边脑区投影的新方法，突破传统仅依赖脑室分割的局限
- 在10名正常人与10名痴呆患者中发现五个邻近区域投影差异显著
- 适合神经影像学研究者用于阿尔茨海默病早期检测
侧脑室（LV）形态变化被广泛用于识别与疾病相关的形态学改变。由于脑室扩大被视为脑萎缩的相对变化，局部纵向脑室变形可反映邻近脑区的形变。然而，传统方法仅基于分割后的脑室掩膜进行分析。本文提出一种基于深度学习的新型方法，首次将脑室周围区域纳入分析，通过匹配基线脑室网格，同时优化基线与随访脑室间对应邻近脑区（如丘脑、尾状核、海马、杏仁核及右侧脑室）的对应点。我们对10名正常人和10名痴呆患者的左侧脑室变形进行了定量评估，结果表明这些邻近区域在脑室表面的投影在两组间存在显著差异。
原文摘要 · Abstract (English)
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.
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