Deep learning-based human CT brain segmentation using MR derived labels
Meera Srikrishna, Joana Periera Periera, Rolf A. Heckemann, Giovanni Volpe, Anna Zettergren, Silke Kern, Eric Westman, Ingmar Gr. Skoog, Michael Schöll · 2020
Computed tomography (CT) is a widely available, low-cost neuroimaging modality primarily used as a brain examination tool for visual assessment of structural brain integrity in neurodegenerative diseases such as dementia disorders. In this study, we developed a deep learning model to expand the applications of CT to morphological brain segmentation and volumetric extraction. We trained densely connected 3D convoluted neural network variants called U-Nets to segment intracranial volume (ICV), grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Dice similarity scores and volumetric Pearson correlation were the evaluation metrics incorporated. Our pilot study created a model that enables automated segmentation in CT with results comparable to magnetic resonance imaging.