Next Article in Journal
Forecasting the Effects of In-Store Marketing on Conversion Rates for Online Shops
Previous Article in Journal
Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data
Article Menu

Export Article

Open AccessArticle
Forecasting 2018, 1(1), 59-69;

Improved Brain Tumor Segmentation via Registration-Based Brain Extraction

Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
Engineering Faculty, University of Magdalena, Santa Marta 470003, Colombia
Management Department, Alumni Association, Concordia University of Edmonton, Edmonton, AB T5B 4E4, Canada
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
Full-Text   |   PDF [7109 KB, uploaded 12 September 2018]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics



[Return to top]
Forecasting EISSN 2571-9394 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top