Journal article ยท Book chapter
Fast, sequence adaptive parcellation of brain MR using parametric models
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark1
Martinos Center for Biomedical Imaging, MIGH, Harvard Medical School, USA.2
In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data.
We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.
Language: | English |
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Publisher: | Springer Berlin Heidelberg |
Year: | 2013 |
Pages: | 727-34 |
ISSN: | 16113349 and 03029743 |
Types: | Journal article and Book chapter |
DOI: | 10.1007/978-3-642-40811-3_91 |
Algorithms Brain Computer Simulation Feasibility Studies Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Models, Anatomic Models, Neurological Models, Statistical Observer Variation Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity