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

• URL: http://arxiv.org/abs/2209.02032v1
• Date: Mon, 5 Sep 2022 16:09:24 GMT
• Title: Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
• Authors: Benjamin Billot, Colin Magdamo, Steven E. Arnold, Sudeshna Das, Juan. E. Iglesias
• Abstract summary: We present SynthSeg+, an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. Specifically, in addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations. 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.
• Score: 1.0499611180329802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm can cope with the high variability in clinical acquisitions (MR contrast, resolution, orientation, etc.). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. Specifically, 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 in wide-ranging settings.

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