TY - GEN
T1 - A deep learning framework for brain extraction in humans and animals with traumatic brain injury
AU - Roy, Snehashis
AU - Knutsen, Andrew
AU - Korotcov, Alexandru
AU - Bosomtwi, Asamoah
AU - Dardzinski, Bernard
AU - Butman, John A.
AU - Pham, Dzung L.
N1 - Funding Information:
∗This work was supported by the Department of Defense in the Center for Neuroscience and Regenerative Medicine and the Intramural Research Program of the National Institutes of Health. This work was also partially supported by grant from National MS Society RG-1507-05243.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - 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.
AB - 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.
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U2 - 10.1109/ISBI.2018.8363667
DO - 10.1109/ISBI.2018.8363667
M3 - Conference contribution
AN - SCOPUS:85048111608
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 687
EP - 691
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -
