Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
This addresses the challenge of unlocking the potential of millions of clinical brain MRI scans for neuroimaging research, which has been hindered by variability in acquisitions, representing a novel method for a known bottleneck.
The authors tackled the problem of analyzing heterogeneous clinical brain MRI datasets by developing SynthSeg+, an AI segmentation suite that enables robust analysis for the first time, accurately replicating atrophy patterns in an ageing study on 14,000 scans.
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 analyse 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, artefacts, subject populations). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.