Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach.

Balsiger, Fabian; Steindel, Carolin; Arn, Mirjam; Wagner, Benedikt; Grunder, Lorenz Nicolas; El-Koussy, Marwan; Valenzuela, Waldo Enrique; Reyes, Mauricio; Scheidegger, Olivier (2018). Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach. Frontiers in neurology, 9(777), p. 777. Frontiers Media S.A. 10.3389/fneur.2018.00777

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Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic studies, and since recently magnetic resonance neurography (MRN). The aim of this study was to develop and evaluate a fully-automatic segmentation method of peripheral nerves of the thigh. T2-weighted sequences without fat suppression acquired on a 3 T MR scanner were retrospectively analyzed in 10 healthy volunteers and 42 patients suffering from clinically and electrophysiologically diagnosed sciatic neuropathy. A fully-convolutional neural network was developed to segment the MRN images into peripheral nerve and background tissues. The performance of the method was compared to manual inter-rater segmentation variability. The proposed method yielded Dice coefficients of 0.859 ± 0.061 and 0.719 ± 0.128, Hausdorff distances of 13.9 ± 26.6 and 12.4 ± 12.1 mm, and volumetric similarities of 0.930 ± 0.054 and 0.897 ± 0.109, for the healthy volunteer and patient cohorts, respectively. The complete segmentation process requires less than one second, which is a significant decrease to manual segmentation with an average duration of 19 ± 8 min. Considering cross-sectional area or signal intensity of the segmented nerves, focal and extended lesions might be detected. Such analyses could be used as biomarker for lesion burden, or serve as volume of interest for further quantitative MRN techniques. We demonstrated that fully-automatic segmentation of healthy and neuropathic sciatic nerves can be performed from standard MRN images with good accuracy and in a clinically feasible time.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Balsiger, Fabian; Wagner, Benedikt; Grunder, Lorenz Nicolas; El-Koussy, Marwan; Valenzuela, Waldo Enrique; Reyes, Mauricio and Scheidegger, Olivier

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1664-2295

Publisher:

Frontiers Media S.A.

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

08 Oct 2018 08:04

Last Modified:

18 Jul 2019 16:35

Publisher DOI:

10.3389/fneur.2018.00777

PubMed ID:

30283397

Uncontrolled Keywords:

health machine learning magnetic resonance imaging magnetic resonance neurography peripheral nervous system diseases sciatic nerve segmentation

BORIS DOI:

10.7892/boris.120352

URI:

https://boris.unibe.ch/id/eprint/120352

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