|We introduce a system that automatically segments and classifies features in 3-D images. The system's accuracy is comparable to manual segmentation. It takes 12 minutes to segment and classify 144 brain structures in 256x256x124 voxel image, while similar work by human took 8 months.
The process starts with an atlas, a hand segmented and classified MRI of a normal brain. Given a subject's data, the atlas ins warped in 3-D using a hierarchical deformable matching algorithm until it closely matches the subject, i.e. the atlas is customized for the subject. The customized atlas contains the segmentation and classification of the subject's anatomical structures.
The system has processed MRI of 105 subjects, and for 97 of them produced segmentation qualitatively comparable to manual segmentation. We performed quantitative evaluation of the precision for one structure. Of 18 subjects for which classification correctness was examined voxel by voxel, an error rate of less than 20% was achieved for 12 subjects, and less than 10% for 5 subjects.
The efficiency, accuracy, and consistency of the system's performance allow for detailed quantitative studies of brain structures. Initial results have been obtained for finding the normal range of variation in the size and symmetry properties of anatomical structures, and for detecting abnormalities.
Grant ID: NAGW-1175
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Project(s): Knowledge-Guided Deformable Registration
Number of pages: 26
|Mei Chen, Takeo Kanade, Henry Rowley, and Dean Pomerleau, "Quantitative Study of Brain Anatomy," tech. report CMU-RI-TR-98-05, Robotics Institute, Carnegie Mellon University, March, 1998|
author = "Mei Chen and Takeo Kanade and Henry Rowley and Dean Pomerleau",
title = "Quantitative Study of Brain Anatomy",
booktitle = "",
institution = "Robotics Institute",
month = "March",
year = "1998",
address= "Pittsburgh, PA",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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