Fast and Robust 3-D MRI Brain Structure Segmentation

  • Authors:

  • Michael Wels;Yefeng Zheng;Gustavo Carneiro;Martin Huber;Joachim Hornegger;Dorin Comaniciu

  • Affiliations:

  • Chair of Pattern Recognition, Department of Computer Science, University Erlangen-Nuremberg, Germany and Siemens CT SE5 SCR2, Erlangen, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton, USA;Institute for Systems and Robotics, Electrical and Computer Engineering Department, Technical University of Lisbon, Portugal;Siemens CT SE5 SCR2, Erlangen, Germany;Chair of Pattern Recognition, Department of Computer Science, University Erlangen-Nuremberg, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton, USA

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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.