Scientists enlist supercomputers, machine learning to automatically identify brain tumors

George Biros, professor of mechanical engineering and

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leader of the ICES Parallel Algorithms for Data Analysis and Simulation Group at The University of Texas at Austin, has worked for nearly a decade to create accurate and efficient computing algorithms that can characterize gliomas, the most common and aggressive type of primary brain tumor.

At the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Biros and collaborators from the University of Pennsylvania (led by Professor Christos Davatzikos), University of Houston (led by Professor Andreas Mang) and University of Stuttgart (led by Professor Miriam Mehl), presented results of a new, fully automatic method that combines biophysical models of tumor growth with machine learning algorithms for the analysis of Magnetic Resonance (MR) imaging data of glioma patients. All the components of the new method were enabled by supercomputers at the Texas Advanced Computing Center (TACC).

Biros' team tested their new method in the Multimodal Brain Tumor Segmentation Challenge 2017 (BRaTS'17), an annual competition where research groups from around the world present methods and results for computer-aided identification and classification of brain tumors, as well as different types of cancerous regions, using pre-operative MR scans.

Read more at: https://phys.org/news/2017-10-scientists-supercomputers-machine-automatically-brain.html#jCp