A comparison of automated and manual segmentation of hippocampus MR images

John W. Haller, Gary E. Christensen, Michael I. Miller, Sarang Joshi, Mokhtar Gado, John Csernansky, Michael W. Vannier

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Purpose: The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods were determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. Methods: MR images of 9 normal subjects and 9 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 = 256 × 256) was chosen which corresponded 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. Results: 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. Conclusions: Quantitative morphometry of brain substructures (e.g. hippocampus) are feasible by use of a high dimensional transformation of a digital brain atlas to a 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.

Original languageEnglish (US)
Pages (from-to)206-215
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2434
DOIs
StatePublished - May 12 1995
Externally publishedYes
EventMedical Imaging 1995: Image Processing - San Diego, United States
Duration: Feb 26 1995Mar 2 1995

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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