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Article in English | MEDLINE | ID: mdl-24111225

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.


Subject(s)
Brain Neoplasms/diagnosis , Brain Neoplasms/pathology , Glioblastoma/diagnosis , Glioblastoma/pathology , Magnetic Resonance Imaging , Algorithms , Artificial Intelligence , Contrast Media , Diagnostic Imaging , Humans , Image Processing, Computer-Assisted , Pattern Recognition, Automated , Predictive Value of Tests , Probability , ROC Curve
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