Deep learning-based human CT brain segmentation using MR derived labels

Meera Srikrishna; Joana Periera Periera; Rolf A. Heckemann M.D.; Giovanni Volpe; Anna Zettergren M.D.; Silke Kern M.D.; Eric Westman; Ingmar Skoog M.D.; Michael Schöll

doi:10.1117/12.2567269

20 August 2020 Deep learning-based human CT brain segmentation using MR derived labels

Meera Srikrishna, Joana Periera Periera, Rolf A. Heckemann M.D., Giovanni Volpe, Anna Zettergren M.D., Silke Kern M.D., Eric Westman, Ingmar Skoog M.D., Michael Schöll

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Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690V (2020) https://doi.org/10.1117/12.2567269
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

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.

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Meera Srikrishna, Joana Periera Periera, Rolf A. Heckemann M.D., Giovanni Volpe, Anna Zettergren M.D., Silke Kern M.D., Eric Westman, Ingmar Skoog M.D., and Michael Schöll "Deep learning-based human CT brain segmentation using MR derived labels", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690V (20 August 2020); https://doi.org/10.1117/12.2567269

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KEYWORDS

Computed tomography

Brain

Tissues

Neuroimaging

Image segmentation

Magnetic resonance imaging

Analytical research

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