实验室研究:从MRI中自动识别灰质结构以改善白质病变的分割

S. Warfield, J. Dengler, J. Zaers, C. Guttmann, W. Wells, G. Ettinger, J. Hiller, R. Kikinis
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引用次数: 14

摘要

由于白质病变(WML)的MRI特征与灰质相似,因此对其MRI扫描进行分割是困难的。基于强度的统计分类技术错误地将一些WML分类为灰质,将一些灰质分类为WML。我们开发了一种快速弹性匹配算法,该算法将包含有关灰质位置信息的参考数据集扭曲成患者大脑的近似形状。在对皮层和深部灰质结构进行分割后,对白质区域进行分割。使用三维区域生长算法识别皮层,该算法受解剖学,强度梯度和组织类别参数的约束。然后使用两类最小距离分类器在不受灰质干扰的情况下对白质和WML进行分割。对16例临床诊断为多发性硬化症(MS)的患者进行双回波自旋回波MRI扫描分析。分割的c…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Laboratory Investigation:Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions
The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML.We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier.Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the c...
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