Predicting a multi-parametric probability map of active tumor extent using random forests

Fred W. Prior, Sarah J. Fouke, Tammie Benzinger, Alicia Boyd, Michael Chicoine, Sharath Cholleti, Matthew Kelsey, Bart Keogh, Lauren Kim, Mikhail Milchenko, David G. Politte, Stephen Tyree, Kilian Weinberger, Daniel Marcus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages6478-6481
Number of pages4
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period07/3/1307/7/13

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    Prior, F. W., Fouke, S. J., Benzinger, T., Boyd, A., Chicoine, M., Cholleti, S., Kelsey, M., Keogh, B., Kim, L., Milchenko, M., Politte, D. G., Tyree, S., Weinberger, K., & Marcus, D. (2013). Predicting a multi-parametric probability map of active tumor extent using random forests. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 6478-6481). [6611038] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6611038