A deep learning framework for brain extraction in humans and animals with traumatic brain injury

Snehashis Roy, Andrew Knutsen, Alexandru Korotcov, Asamoah Bosomtwi, Bernard Dardzinski, John A. Butman, Dzung L. Pham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Automatic brain extraction or skull stripping from magnetic resonance images (MRI) is an important pre-processing step in many image processing pipelines. Most skull stripping methods are optimized for normal brains and applicable to single T1-w MR images. However, other contrasts, such as T2, can provide complementary information about the boundary. This is especially true in the presence of traumatic brain injury (TBI) and other diseases, where lesions can confound boundary definitions. In this paper, we propose a deep learning based framework to extract intracranial tissues from multi-contrast MR images in the presence of TBI. Our approach is based on state-of-the-art convolutional neural network architecture to learn a transformation from multi-contrast atlas MR images to their stripping masks without using any deformable registration. An advantage of our framework is that it can be applied to different species. We applied our approach to 19 human patients with mild to severe TBI, as well as 16 normal mice images, and another 10 mice brains with TBI. We compared the approach with 3 separate state-of-the-art human and rodent brain extraction methods. Using only a few manually delineated atlases, we showed significant improvement in brain extraction accuracy in both healthy and pathological human and rodent images.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages687-691
Number of pages5
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Roy, S., Knutsen, A., Korotcov, A., Bosomtwi, A., Dardzinski, B., Butman, J. A., & Pham, D. L. (2018). A deep learning framework for brain extraction in humans and animals with traumatic brain injury. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 687-691). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363667