Hippocampal Shape Recovery & Analysis in Epileptics

描述(由申请人提供)： 该项目旨在从人脑MRI中自动恢复和分析3D海马区的形状，并将癫痫灶定位到合适的颞叶。假设左侧和右侧海马体的形状差异(而不是体积)将把癫痫患者与健康对照组区分开来，并确定癫痫灶的半球位置。我们提出了三个阶段的解决方案来检验这一假设：(A)开发学习算法来创建用于分割的海马体形状和图像图谱，(B)自动(基于图谱)海马体分割和验证，以及(C)患者扫描的自动分类和分类器的验证。 所提议的新分割方案将涉及基于图集的方法，其中图集是使用一种新的学习算法从预期数据集构建的，该学习算法基于从给定的适配形状的给定群体中找到作为最小距离适配形状的图集形状。类似地学习的MR图像图谱将被用来增强先前学习的形状。然后，学习的地图集将被用来估计从回顾数据档案中实现未知对象扫描的基于地图集的分割所需的非刚性变形场。提出了一种新的基于核Fischer判别式的分类器，用于自动将受试者分为对照组和癫痫灶位于左叶或右叶的受试者。 分割和分类算法将在合成的和真实的来自档案的磁共振脑扫描上得到验证。所提出的算法方案有相当大的潜力用于其他解剖结构的分割和分析，因此对其他疾病状态具有实用价值。

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.