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Your search keyword '"Vernooij, M."' showing total 4 results
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4 results on '"Vernooij, M."'

1. Orientation Prior and Consistent Model Selection Increase Sensitivity of Tract-Based Spatial Statistics in Crossing-Fiber Regions.

Author

Arkesteijn, G. A. M., Poot, D. H. J., Ikram, M. A., Niessen, W. J., Van Vliet, L. J., Vernooij, M. W., and Vos, F. M.

Subjects
DIFFUSION magnetic resonance imaging, FIBER orientation, STATISTICS, INTRACLASS correlation, DIFFUSION coefficients, STATISTICAL power analysis
Abstract

The goal of this paper is to increase the statistical power of crossing-fiber statistics in voxelwise analyses of diffusion-weighted magnetic resonance imaging (DW-MRI) data. In the proposed framework, a fiber orientation atlas and a model complexity atlas were used to fit the ball-and-sticks model to diffusion-weighted images of subjects in a prospective population-based cohort study. Reproducibility and sensitivity of the partial volume fractions in the ball-and-sticks model were analyzed using TBSS (tract-based spatial statistics) and compared to a reference framework. The reproducibility was investigated on two scans of 30 subjects acquired with an interval of approximately three weeks by studying the intraclass correlation coefficient (ICC). The sensitivity to true biological effects was evaluated by studying the regression with age on 500 subjects from 65 to 90 years old. Compared to the reference framework, the ICC improved significantly when using the proposed framework. Higher t-statistics indicated that regression coefficients with age could be determined more precisely with the proposed framework and more voxels correlated significantly with age. The application of a fiber orientation atlas and a model complexity atlas can significantly improve the reproducibility and sensitivity of crossing-fiber statistics in TBSS. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Asymmetric similarity-weighted ensembles for image segmentation

Author

Cheplygina, V., Van Opbroek, A., Ikram, M. A., Vernooij, M. W., de Bruijne, Marleen, Cheplygina, V., Van Opbroek, A., Ikram, M. A., Vernooij, M. W., and de Bruijne, Marleen

Abstract

Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task.

Published
2016

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