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15 May 2023 Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net
Sabien van Elst, Christiaan M. de Bloeme, Samantha Noteboom, Marcus C. de Jong, Annette C. Moll, Sophia Göricke, Pim De Graaf, Matthan W. A. Caan
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Journal of Medical Imaging, Vol. 10, Issue 3, 034501 (May 2023). https://doi.org/10.1117/1.JMI.10.3.034501
Abstract

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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Sabien van Elst, Christiaan M. de Bloeme, Samantha Noteboom, Marcus C. de Jong, Annette C. Moll, Sophia Göricke, Pim De Graaf, and Matthan W. A. Caan "Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net," Journal of Medical Imaging 10(3), 034501 (15 May 2023). https://doi.org/10.1117/1.JMI.10.3.034501
Received: 26 November 2022; Accepted: 19 April 2023; Published: 15 May 2023
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KEYWORDS
Image segmentation
Magnetic resonance imaging
3D modeling
Optic nerve
Nerve
Visualization
3D image processing
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