Medicine and Health Sciences Commons^™

Brain Extraction On Mri Scans In Presence Of Diffuse Glioma: Multi-Institutional Performance Evaluation Of Deep Learning Methods And Robust Modality-Agnostic Training, Siddhesh Thakur, Jimit Doshi, Sarthak Pati, Saima Rathore, Chiharu Sako, Michel Bilello, Sung Min Ha, Gaurav Shukla, Adam Flanders, Aikaterini Kotrotsou, Mikhail Milchenko, Spencer Liem, Bs, Gregory S Alexander, Joseph Lombardo, Joshua D Palmer, Pamela Lamontagne, Arash Nazeri, Sanjay Talbar, Uday Kulkarni, Daniel Marcus, Rivka Colen, Christos Davatzikos, Guray Erus, Spyridon Bakas

Department of Radiation Oncology Faculty Papers

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI …