Hippocampal Shape Recovery & Analysis in Epileptics

描述（由申请人提供）： 本项目旨在从人脑MRI中自动恢复和分析海马的三维形状，并将癫痫灶定位到适当的颞叶。该假说是左右侧大脑半球的形状差异（而不是体积）将区分癫痫患者和健康对照者，并确定癫痫病灶的半球位置。我们提出了一个三阶段的解决方案来测试这一假设：（a）学习算法的开发，以创建海马形状和图像图谱用于分割，（B）自动（基于图谱）海马分割和验证，以及（c）自动分类的患者扫描和验证的分类器。 所提出的新分割方案将涉及基于图谱的方法，其中图谱是使用基于从给定的拟合形状群体中找到作为最小距离拟合形状的图谱形状的新型学习算法从预期数据集构建的。类似地学习的MR图像图谱将用于增强学习的形状先验。然后，将采用所学习的图谱来估计从回顾性数据的存档中实现未知受试者扫描的基于图谱的分割所需的非刚性变形场。提出了一种新的分类器的基础上的核Fischer判别式自动分类科目成组对应的控制和癫痫灶定位于左或右叶。 分割和分类算法将在存档的合成和真实的MR脑部扫描上进行验证。所提出的算法方案在其他解剖结构的分割和分析中具有相当大的潜力，因此对其他疾病状态具有实用性。

DESCRIPTION (provided by applicant): This project aims to automatically recover and analyze the 3D hippocampal shape from human brain MRI and localize the epileptic focus to the appropriate temporal lobe. The hypothesis is that shape differences (not volume) between the left and right hippocampi will distinguish patients with epilepsy from healthy controls and identify the hemispheric location of the epileptic focus. We propose a three phase solution to test this hypothesis: (a) the development of a learning algorithm to create hippocampal shape and image atlases for use in segmentation, (b) automatic (atlas-based) hippocampal segmentation and validation, and (c) automatic classification of patient scans and validation of the classifier. The proposed new segmentation scheme will involve an atlas-based approach wherein the atlas is constructed from a prospective data set using a novel learning algorithm based on finding the atlas shape as the minimum distance fitted shape from the given population of fitted shapes. An MR image atlas learned similarly will be used to augment the learned shape prior. The learnt atlases will then be employed for estimating the non-rigid deformation field required to achieve an atlas-based segmentation of the unknown subject scan from an archive of retrospective data. A novel classifier based on the Kernel Fischer discriminant is proposed for automatically classifying subjects into groups corresponding to controls and those with epileptic foci localized to either the left or the right lobe. The segmentation and the classification algorithms will be validated on synthetic and real MR brain scans from an archive. The proposed algorithmic schemes have considerable potential for use in the segmentation and analysis of other anatomical structures and thus have utility for other disease states.