@inproceedings{3be55fa6ae074e14b1464a5431049b43,
title = "Predicting a multi-parametric probability map of active tumor extent using random forests",
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.",
author = "Prior, {Fred W.} and Fouke, {Sarah J.} and Tammie Benzinger and Alicia Boyd and Michael Chicoine and Sharath Cholleti and Matthew Kelsey and Bart Keogh and Lauren Kim and Mikhail Milchenko and Politte, {David G.} and Stephen Tyree and Kilian Weinberger and Daniel Marcus",
year = "2013",
doi = "10.1109/EMBC.2013.6611038",
language = "English",
isbn = "9781457702167",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "6478--6481",
booktitle = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013",
note = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 ; Conference date: 03-07-2013 Through 07-07-2013",
}