Predicting a multi-parametric probability map of active tumor extent using random forests
Fred Prior, Sarah Jost Fouke, Tammie L.S. Benzinger, Alicia Boyd, Michael R. Chicoine, Sharath R. Cholleti, Matthew Kelsey, Bart P. Keogh, Lauren Kim, Mikhail V. Milchenko, David G. Politte, Stephen Tyree, Kilian Q. Weinberger, Daniel S. Marcus · 2013
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.