Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
University College London · Harvard University · +3 more institutions
Abstract
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research. Yet, their potential remains untapped since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artifacts, and subject populations). Here, we present SynthSeg + , an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg + also performs cortical parcellation, intracranial volume estimation, and automated detection of…
Citation impact
- FWCI
- 23.68
- Percentile
- 100%
- References
- 56
Authors
6- BBBenjamin BillotCorresponding
University College London
- CMColin Magdamo
Harvard University, Massachusetts General Hospital
- YCYou Cheng
Harvard University, Massachusetts General Hospital
- SESteven E. Arnold
Harvard University, Massachusetts General Hospital
- SDSudeshna Das
Harvard University, Massachusetts General Hospital
Topics & keywords
- Segmentation
- Neuroimaging
- Artificial intelligence
- Computer science
- Suite
- Pattern recognition (psychology)
- Brain morphometry
- Brain size