A learning based algorithm for automatic extraction of the cortical sulci

Authors:
Songfeng Zheng;Zhuowen Tu;Alan L. Yuille;Allan L. Reiss;Rebecca A. Dutton;Agatha D. Lee;Albert M. Galaburda;Paul M. Thompson;Ivo Dinov;Arthur W. Toga
Affiliations:
Department of Statistics, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, UCLA Medical School, Los Angeles, CA;Department of Statistics, UCLA, Los Angeles, CA;School of Medicine, Stanford University, Stanford, CA;Laboratory of Neuro Imaging, UCLA Medical School, Los Angeles, CA;Laboratory of Neuro Imaging, UCLA Medical School, Los Angeles, CA;School of Medical, Harvard University, Cambridge, MA;Laboratory of Neuro Imaging, UCLA Medical School, Los Angeles, CA;Department of Statistics, UCLA, Los Angeles, CA;Laboratory of Neuro Imaging, UCLA Medical School, Los Angeles, CA
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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Abstract

This paper presents a learning based method for automatic extraction of the major cortical sulci from MRI volumes or extracted surfaces. Instead of using a few pre-defined rules such as the mean curvature properties, to detect the major sulci, the algorithm learns a discriminative model by selecting and combining features from a large pool of candidates. We used the Probabilistic Boosting Tree algorithm [16] to learn the model, which implicitly discovers and combines rules based on manually annotated sulci traced by neuroanatomists. The algorithm almost has no parameters to tune and is fast because of the adoption of integral volume and 3D Haar filters. For a given approximately registered MRI volume, the algorithm computes the probability of how likely it is that each voxel lies on a major sulcus curve. Dynamic programming is then applied to extract the curve based on the probability map and a shape prior. Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect makes the approach flexible and it also works on extracted cortical surfaces.