Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

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 <i>SynthSeg</i>⁺, an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, <i>SynthSeg</i>⁺ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate <i>SynthSeg</i>⁺ in seven experiments, including an aging study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. <i>SynthSeg</i>⁺ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.

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