OFF-eNET: An optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation

Nazir, A., Cheema, M., Sheng, B. et al. (7 more authors) (2020) OFF-eNET: An optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation. IEEE Transactions on Image Processing. ISSN 1057-7149

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Copyright, Publisher and Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Convolution neural network; computed tomography angiography; dilated convolution; inception module; upskip connection; intracranial vessels segmentation
Dates:
  • Accepted: 31 May 2020
  • Published (online): 9 June 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 05 Jun 2020 14:37
Last Modified: 29 Jun 2020 15:00
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/TIP.2020.2999854

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