Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions

Abstract

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 cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion. J Image Guid Surg 1:326–338 (1995). © 1996 Wiley‐Liss, Inc.

Serial registration of intraoperative MR images of the brain

“…Our laboratory has had extensive experience with image-based matching models for a variety of brain matching applications (Warfield et al, 1995;Warfield, 1996;Nakajima et al, 1997;Hata et al, 1998;Warfield et al, 1999;Ferrant et al, 1999b;Hata et al, 2000;Kaus, 2000;Ruiz-Alzola et al, 2001;Rexilius and Warfield, 2001). However, in the context of intraoperative brain matching, such methods have not yet been able to provide a robust and clinically applicable solution.…”

“…This means we can use segmentation approaches that are robust and accurate but are time consuming and hence impractical to use in the operating room. In our laboratory, preoperative data is segmented with a variety of manual (Gering et al, 1999), semi-automated (Kikinis et al, 1992) or automated (Warfield et al, 1995Kaus et al, 2000) approaches. We select the most robust and accurate approach available for a given clinical application.…”

Automatic brain tumor segmentation by subject specific modification of atlas priors1

“…Warfield et al (10,11) combined elastic atlas registration with statistical classification. Elastic registration of a brain atlas helped to mask the brain from surrounding structures.…”

Fast re-rendering of volume and surface graphics by depth, color, and opacity buffering

“…This may be satisfactory when the data to be visualized has already been segmented, e.g. using accurate statistical methods (Wells et al, 1996a) (Warfield et al, 1995), since the model surfaces have been defined. The time penalty for segmentation, whether manual or semi-automatic, and model generation is tolerated to gain real time performance during visualization.…”

“…It has already been demonstrated that it is possible to perform pre-operative segmentation by elastic matching of surfaces e.g. (Warfield et al, 1995). However, for intraoperative segmentation and virtual surgery simulation, physical tissue modeling using voxels is being advocated (Gibson, 1997) -hence the need for volume graphics.…”