Characterizing brain tumor subtypes from MRI images – a computational approach


Brain tumors are the second most common malignancy in childhood after leukemia. Magnetic resonance imaging (MRI) is a popular clinical method to diagnose brain tumors due to the fact that it is a non-invasive, painless procedure without any ionizing radiation. The standard pipeline for diagnosis after generating MRI scans require clinicians’ expert examination to pinpoint location, size, and types of brain tumor. To assist in the examination, there are also several proprietary tools exist that offer basic image analysis, including segmentation based on histograms, etc. Automated secondary analysis on the images is still performed manually by a clinician. There is a need to develop a tool to help reducing clinicians’ average investigation time on an image by developing a prediction algorithm leveraging the power of the deep neural network to determine the four subtypes of brain tumor: medulloblastomas, DIPG, ependymomas, and edema.

Supporting materials