Literature DB >> 7850740

Interactive segmentation of cerebral gray matter, white matter, and CSF: photographic and MR images.

T Q Bartlett1, M W Vannier, D W McKeel, M Gado, C F Hildebolt, R Walkup.   

Abstract

Digital photography of postmortem brain slices was compared with magnetic resonance imaging (MRI) for morphological analysis of human brain atrophy. In this study, we used two human brains obtained at autopsy: a cognitively defined nondemented control (70-yr-old male) and a demented Alzheimer's disease (AD) subject (82-yr-old female). For each of two brains, interactive manual image segmentation was performed by two observers on two image sets: (a) four coronal T1-weighted MR images (5 mm slices); and (b) four digitized photographic images from comparable rostrocaudal levels. Microcomputer image analysis software was used to measure the areas of three segmented cerebral compartments--gray matter (GM), white matter (WM) and CSF--for both image types. Resegmentation error was defined as the absolute difference between the areas derived from two segmentation trials divided by the value from trial 1 and multiplied by 100. This yielded the percent difference between the area measurements from the two trials. We found intra-observer agreement was better (error rates 1-18%) than inter-observer agreement (3-70%) with best agreement for WM and least for CSF, the smallest object class. MRI overestimated GM area relative to digitized photographs in the control but not the AD brain. The results define limitations of manual image segmentations and comparison of MRI with pathologic section photographic images.

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Year:  1994        PMID: 7850740     DOI: 10.1016/0895-6111(94)90083-3

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  2 in total

1.  An electrostatic deformable model for medical image segmentation.

Authors:  Herng-Hua Chang; Daniel J Valentino
Journal:  Comput Med Imaging Graph       Date:  2007-10-15       Impact factor: 4.790

2.  Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning.

Authors:  Zhenglun Kong; Ting Li; Junyi Luo; Shengpu Xu
Journal:  J Healthc Eng       Date:  2019-01-31       Impact factor: 2.682

  2 in total

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