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

Author:

Billot Benjamin1ORCID,Magdamo Colin2,Cheng You2,Arnold Steven E.2ORCID,Das Sudeshna2ORCID,Iglesias Juan Eugenio134

Affiliation:

1. Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK

2. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114

3. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02129

4. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02138

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 faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg + in seven experiments, including an aging 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.

Funder

EC | European Research Council

UKRI | EPSRC | EPSRC Centre for Doctoral Training in Medical Imaging

Alzheimer's Research UK

HHS | NIH | National Institute on Aging

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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