Open AccessImproving the SIENA performance using BEaST brain extraction
Author(s) -
Kunio Nakamura,
Simon Fristed Eskildsen,
Sridar Narayanan,
Douglas L. Arnold,
D. Louis Collins
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0196945
Subject(s) - reproducibility , brain size , medicine , segmentation , nuclear medicine , magnetic resonance imaging , mathematics , computer science , artificial intelligence , statistics , radiology
We present an improved image analysis pipeline to detect the percent brain volume change (PBVC) using SIENA ( S tructural I mage E valuation, using N ormalization, of A trophy) in populations with Alzheimer’s dementia. Our proposed approach uses the improved brain extraction mask from BEaST ( B rain E xtraction based on nonloc a l S egmentation T echnique) instead of the conventional BET ( B rain E xtraction T ool) for SIENA. We compared four varying options of BET as well as BEaST and applied these five methods to analyze scan-rescan MRIs in ADNI from 332 subjects, longitudinal ADNI MRIs from the same 332 subjects, their repeat scans over time, and OASIS longitudinal MRIs from 123 subjects. The results showed that BEaST brain masks were consistent in scan-rescan reproducibility. The cross-sectional scan-rescan error in the absolute percent brain volume difference measured by SIENA was smallest ( p ≤0.0187) with the proposed BEaST-SIENA . We evaluated the statistical power in terms of effect size, and the best performance was achieved with BEaST-SIENA (1.2789 for ADNI and 1.095 for OASIS). The absolute difference in PBVC between scan-dataset (volume change from baseline to year-1) and rescan-dataset (volume change from baseline repeat scan to year-1 repeat scan) was also the smallest with BEaST-SIENA compared to the BET-based SIENA and had the highest correlation when compared to the BET-based SIENA variants. In conclusion, our study shows that BEaST was robust in terms of reproducibility and consistency and that SIENA’s reproducibility and statistical power are improved in multiple datasets when used in combination with BEaST.