Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors

Authors:
Fedde van der Lijn;Marleen de Bruijne;Yoo Young Hoogendam;Stefan Klein;Reinhard Hameeteman;Monique M. B. Breteler;Wiro J. Niessen
Affiliations:
Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Department of Computer Science, University of Copenhagen, Copenhage ...;Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Imaging Science & Technology, Faculty of Applied Science, Delft ...
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

We propose a novel cerebellum segmentationmethod for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image descriptors as features. The spatial model is constructed by registering multiple manually annotated datasets to the unlabeled target image. The two components are then combined in a Bayesian framework. The method is quantitatively validated in a leave-one-out experiment using 18 MR images of elderly subjects. The experiment showed that the method produces accurate segmentations. The mean Dice similarity index compared to the manual reference was 0.953 for left and right, and the mean surface distance was 0.49 mm for left and 0.50 mm for right. The combined atlasand appearance-based method was found to be more accurate than a method based on atlas-registration alone.