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Forecasting 2018, 1(1), 59-69; https://doi.org/10.3390/forecast1010005

Improved Brain Tumor Segmentation via Registration-Based Brain Extraction

1
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
2
Engineering Faculty, University of Magdalena, Santa Marta 470003, Colombia
3
Management Department, Alumni Association, Concordia University of Edmonton, Edmonton, AB T5B 4E4, Canada
4
Cross Cancer Institute of Alberta, Edmonton, AB T6G 1Z2, Canada
*
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 17 August 2018 / Accepted: 5 September 2018 / Published: 12 September 2018
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Abstract

Automated brain tumor segmenters typically run a “skull-stripping” pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved by replacing its skull-stripper component with one that instead uses a registration-based approach. In particular, we compare our automated brain segmentation system with the original system as well as three other approaches that differ only by using a different skull-stripper—BET, HWA, and ROBEX: (1) Over scans of 120 patients with brain tumors, our system’s segmentation accuracy (Dice score with respect to expert segmentation) is 8.6% (resp. 2.7%) better than the original system on gross tumor volumes (resp. edema); (2) Over 103 scans of controls, the new system found 92.9% (resp. 57.8%) fewer false positives on T1C (resp. FLAIR) volumes. (The other three methods were significantly worse on both tasks). Finally, the new registration-based approach is over 15% faster than the original, requiring on average only 178 CPU seconds per volume. View Full-Text
Keywords: tumor segmentation; skull extraction; brain extraction; registration; BET; HWA; ROBEX tumor segmentation; skull extraction; brain extraction; registration; BET; HWA; ROBEX
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Uhlich, M.; Greiner, R.; Hoehn, B.; Woghiren, M.; Diaz, I.; Ivanova, T.; Murtha, A. Improved Brain Tumor Segmentation via Registration-Based Brain Extraction. Forecasting 2018, 1, 59-69.

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