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Fast and Robust 3-D MRI Brain Structure Segmentation

Wels M, Zheng Y, Carneiro G, Huber M, Hornegger J, Comaniciu D (2009)

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Publication Type: Conference contribution

Publication year: 2009

Journal

Publisher: Springer-verlag

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

City/Town: Heidelberg

Book Volume: Lecture Notes on Computer Science 5762

Pages Range: 575-583

Conference Proceedings Title: Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Part II, LNCS 5762

Event location: London, UK

ISBN: 3-642-04270-8

URI: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2009/Wels09-FAR.pdf

DOI: 10.1007/978-3-642-04271-3_70

Abstract

We present a novel method for the automatic detection and segmentation of (sub-)cortical gray matter structures in 3-D magnetic resonance images of the human brain. Essentially, the method is a top-down segmentation approach based on the recently introduced concept of Marginal Space Learning (MSL). We show that MSL naturally decomposes the parameter space of anatomy shapes along decreasing levels of geometrical abstraction into subspaces of increasing dimensionality by exploiting parameter invariance. At each level of abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. Contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-D generalized Haar features and steerable features derived from the observed volume intensities. Our system allows us to detect and segment 8 (sub-)cortical gray matter structures in T1-weighted 3-D MR brain scans from a variety of different scanners in on average 13.9 sec., which is faster than most of the approaches in the literature. In order to ensure comparability of the achieved results and to validate robustness, we evaluate our method on two publicly available gold standard databases consisting of several T1-weighted 3-D brain MR scans from different scanners and sites. The proposed method achieves an accuracy better than most state-of-the-art approaches using standardized distance and overlap metrics. © 2009 Springer-Verlag.

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How to cite

APA:

Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and Robust 3-D MRI Brain Structure Segmentation. In Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Part II, LNCS 5762 (pp. 575-583). London, UK: Heidelberg: Springer-verlag.

MLA:

Wels, Michael, et al. "Fast and Robust 3-D MRI Brain Structure Segmentation." Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), London, UK Heidelberg: Springer-verlag, 2009. 575-583.

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