Human brain extraction with deep learning

Hao Li; Qibang Zhu; Dewei Hu; Manasvi R. Gunnala; Hans Johnson; Omar Sherbini; Francesco Gavazzi; Russell D’Aiello; Adeline Vanderver; Jeffrey D. Long; Jane S. Paulsen; Ipek Oguz

doi:10.1117/12.2613277

4 April 2022 Human brain extraction with deep learning

Hao Li, Qibang Zhu, Dewei Hu, Manasvi R. Gunnala, Hans Johnson, Omar Sherbini, Francesco Gavazzi, Russell D’Aiello, Adeline Vanderver, Jeffrey D. Long, Jane S. Paulsen, Ipek Oguz

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120321D (2022) https://doi.org/10.1117/12.2613277
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Brain extraction, also known as skull stripping, from magnetic resonance images (MRIs) is an essential preprocessing step for many medical image analysis tasks and is also useful as a stand-alone task for estimating the total brain volume. Currently, many proposed methods have excellent performance on T1-weighted images, especially for healthy adults. However, such methods do not always generalize well to more challenging datasets such as pediatric, severely pathological, or heterogeneous data. In this paper, we propose an automatic deep learning framework for brain extraction on T1-weighted MRIs of adult healthy controls, Huntington’s disease patients and pediatric Aicardi Gouti`eres Syndrome (AGS) patients. We examine our method on the PREDICT-HD and the AGS datasets, which are multi-site datasets with different protocols/scanners. Compared to current state-of-the-art methods, our method produced the best segmentations with the highest Dice score, lowest average surface distance and lowest 95-percent Hausdorff distance on both datasets. These results indicate that our method has better accuracy and generalizability for heterogeneous T1-w MRI datasets.

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Hao Li, Qibang Zhu, Dewei Hu, Manasvi R. Gunnala, Hans Johnson, Omar Sherbini, Francesco Gavazzi, Russell D’Aiello, Adeline Vanderver, Jeffrey D. Long, Jane S. Paulsen, and Ipek Oguz "Human brain extraction with deep learning", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321D (4 April 2022); https://doi.org/10.1117/12.2613277

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