Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net.

Autor:	van Elst S; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands., de Bloeme CM; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands., Noteboom S; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, The Netherlands., de Jong MC; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands., Moll AC; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Ophthalmology, Amsterdam, The Netherlands., Göricke S; University Hospital Essen, Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen, Germany., de Graaf P; Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands., Caan MWA; Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2023 May; Vol. 10 (3), pp. 034501. Date of Electronic Publication: 2023 May 15.
DOI:	10.1117/1.JMI.10.3.034501
Abstrakt:	Purpose: Pathological conditions associated with the optic nerve (ON) can cause structural changes in the nerve. Quantifying these changes could provide further understanding of disease mechanisms. We aim to develop a framework that automatically segments the ON separately from its surrounding cerebrospinal fluid (CSF) on magnetic resonance imaging (MRI) and quantifies the diameter and cross-sectional area along the entire length of the nerve. Approach: Multicenter data were obtained from retinoblastoma referral centers, providing a heterogeneous dataset of 40 high-resolution 3D T2-weighted MRI scans with manual ground truth delineations of both ONs. A 3D U-Net was used for ON segmentation, and performance was assessed in a tenfold cross-validation ( n = 32 ) and on a separate test-set ( n = 8 ) by measuring spatial, volumetric, and distance agreement with manual ground truths. Segmentations were used to quantify diameter and cross-sectional area along the length of the ON, using centerline extraction of tubular 3D surface models. Absolute agreement between automated and manual measurements was assessed by the intraclass correlation coefficient (ICC). Results: The segmentation network achieved high performance, with a mean Dice similarity coefficient score of 0.84, median Hausdorff distance of 0.64 mm, and ICC of 0.95 on the test-set. The quantification method obtained acceptable correspondence to manual reference measurements with mean ICC values of 0.76 for the diameter and 0.71 for the cross-sectional area. Compared with other methods, our method precisely identifies the ON from surrounding CSF and accurately estimates its diameter along the nerve's centerline. Conclusions: Our automated framework provides an objective method for ON assessment in vivo . (© 2023 The Authors.)
Databáze:	MEDLINE
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