Development and evaluation of an automated EPTN-consensus based organ at risk atlas in the brain on MRI

Crouzen, J. A.; Petoukhova, A.; Wiggenraad, Ruud; Hutschemaekers, S.; Hooijdonk, Christa G.M. Gadellaa-van; Zyp, Noëlle C.M.G. van der Voort van; Mast, M.; Zindler, Jaap

doi:10.1016/j.radonc.2022.06.004

Cited by 3 publications

(2 citation statements)

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“…Panda et al 13 presented a multiatlas labeling approach for segmenting the eyes, ONs with surrounding CSF, and optic chiasm from high-resolution 3D T2-weighted MR images. Crouzen et al 14 developed an MR-based organs-at-risk (OARs) segmentation atlas from cerebral T1-weighted MR scans to automatically delineate OARs, including the ONs, for radiotherapy planning. However, their ON segmentations required frequent manual adjustments to achieve clinical acceptability.…”

Section: Introductionmentioning

confidence: 99%

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

van Elst,

de Bloeme,

Noteboom

et al. 2023

J. Med. Imag.

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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 ¼ 32) and on a separate test-set (n ¼ 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.

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Section: Introductionmentioning

confidence: 99%

“…Crouzen et al. 14 developed an MR-based organs-at-risk (OARs) segmentation atlas from cerebral T1-weighted MR scans to automatically delineate OARs, including the ONs, for radiotherapy planning. However, their ON segmentations required frequent manual adjustments to achieve clinical acceptability.…”

Section: Introductionmentioning

confidence: 99%