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Proceedings Paper

Comparison of automated and manual segmentation of hippocampus MR images
Author(s): John W. Haller; Gary E. Christensen; Michael I. Miller; Sarang C. Joshi; Mokhtar Gado M.D.; John G. Csernansky M.D.; Michael W. Vannier M.D.
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Paper Abstract

The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods are determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. MR images of nine normal subjects and nine schizophrenic patients were acquired with a 1.5-T unit (Siemens Medical Systems, Inc., Iselin, New Jersey). From each individual MPRAGE 3D volume image a single comparable 2-D slice (matrix equals 256 X 256) was chosen which corresponds to the same coronal slice of the hippocampus. The hippocampus was first manually segmented, then segmented using high dimensional transformations of a digital brain atlas to individual brain MR images. The repeatability of a trained rater was assessed by comparing two measurements from each individual subject. Variability was also compared within and between subject groups of schizophrenics and normal subjects. Finally, the precision and accuracy of automated segmentation of hippocampal areas were determined by comparing automated measurements to manual segmentation measurements made by the trained rater on MR and brain slice images. The results demonstrate the high repeatability of area measurement from MR images of the human hippocampus. Automated segmentation using high dimensional transformations from a digital brain atlas provides repeatability superior to that of manual segmentation. Furthermore, the validity of automated measurements was demonstrated by a high correlation with manual segmentation measurements made by a trained rater. Quantitative morphometry of brain substructures (e.g. hippocampus) is feasible by use of a high dimensional transformation of a digital brain atlas to an individual MR image. This method automates the search for neuromorphological correlates of schizophrenia by a new mathematically robust method with unprecedented sensitivity to small local and regional differences.

Paper Details

Date Published: 12 May 1995
PDF: 10 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208692
Show Author Affiliations
John W. Haller, Washington Univ. School of Medicine (United States)
Gary E. Christensen, Washington Univ. School of Medicine (United States)
Michael I. Miller, Washington Univ. School of Medicine (United States)
Sarang C. Joshi, Washington Univ. School of Medicine (United States)
Mokhtar Gado M.D., Washington Univ. School of Medicine (United States)
John G. Csernansky M.D., Washington Univ. School of Medicine (United States)
Michael W. Vannier M.D., Washington Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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