SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests.
Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38-42 weeks gestational age), children and adolescents (4-17 years) and adults (35-71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course.
MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK.
Ahmed
A
Serag
Department of Radiology, Royal Hospital for Sick Children Edinburgh, UK.
Alastair G
AG
Wilkinson
MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK.
Emma J
EJ
Telford
MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK.
Rozalia
R
Pataky
MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK.
Sarah A
SA
Sparrow
MRC Centre for Reproductive Health, University of EdinburghEdinburgh, UK; Centre for Clinical Brain Sciences, University of EdinburghEdinburgh, UK.
Devasuda
D
Anblagan
Clinical Research Imaging Centre, University of Edinburgh Edinburgh, UK.
Gillian
G
Macnaught
Clinical Research Imaging Centre, University of EdinburghEdinburgh, UK; Centre for Cardiovascular Science, University of EdinburghEdinburgh, UK.
Scott I
SI
Semple
MRC Centre for Reproductive Health, University of EdinburghEdinburgh, UK; Centre for Clinical Brain Sciences, University of EdinburghEdinburgh, UK.
James P
JP
Boardman
MRI; brain; classification; large-scale; life-course; random forests; sliding window; tissue segmentation