  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.
