Fast and uncertainty-aware cerebral cortex morphometry estimation using random forest regression
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2018
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04B - Conference paper
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2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
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1052-1055
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IEEE
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Washington, DC
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Abstract
The cortical thickness and curvature of the human brain have proven to be valuable markers to detect and monitor neurodegenerative diseases [1]. Since the computational burden of currently available tools for brain morphometry is very high, this analysis often is only used for retrospective studies and not routinely in the clinics. A first attempt at a clinical use of cortical morphology is reported in [2]. We present an experiment for fast morphometry estimations using Random Forest (RF) regression [3] directly from MR imaging data. An uncertainty-aware voxel-wise, parcellation-wise, and multioutput model was built to estimate the thickness and mean curvature of the human cerebral cortex in 15 minutes instead of many hours for mesh-based tools. Preliminary results on a healthy controls database with 315 subjects show a substantial bias for the voxel-wise prediction, but high scan-rescan robustness, the proposed multi-output-parcellation prediction demonstrates the feasibility of the approach.
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IEEE International Symposium on Biomedical Imaging
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978-1-5386-3636-7
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English
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No
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Published
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peer-reviewed
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Suter, Y., Rummel, C., Wiest, R., & Reyes, M. (2018). Fast and uncertainty-aware cerebral cortex morphometry estimation using random forest regression. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 1052–1055. https://doi.org/10.1109/ISBI.2018.8363752