Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma.

Détails

ID Serval
serval:BIB_61A3D7FC211B
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma.
Périodique
Scientific reports
Auteur(s)
Strijbis VIJ, de Bloeme C.M., Jansen R.W., Kebiri H., Nguyen H.G., de Jong M.C., Moll A.C., Bach-Cuadra M., de Graaf P., Steenwijk M.D.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Statut éditorial
Publié
Date de publication
16/07/2021
Peer-reviewed
Oui
Volume
11
Numéro
1
Pages
14590
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
In retinoblastoma, accurate segmentation of ocular structure and tumor tissue is important when working towards personalized treatment. This retrospective study serves to evaluate the performance of multi-view convolutional neural networks (MV-CNNs) for automated eye and tumor segmentation on MRI in retinoblastoma patients. Forty retinoblastoma and 20 healthy-eyes from 30 patients were included in a train/test (N = 29 retinoblastoma-, 17 healthy-eyes) and independent validation (N = 11 retinoblastoma-, 3 healthy-eyes) set. Imaging was done using 3.0 T Fast Imaging Employing Steady-state Acquisition (FIESTA), T2-weighted and contrast-enhanced T1-weighted sequences. Sclera, vitreous humour, lens, retinal detachment and tumor were manually delineated on FIESTA images to serve as a reference standard. Volumetric and spatial performance were assessed by calculating intra-class correlation (ICC) and dice similarity coefficient (DSC). Additionally, the effects of multi-scale, sequences and data augmentation were explored. Optimal performance was obtained by using a three-level pyramid MV-CNN with FIESTA, T2 and T1c sequences and data augmentation. Eye and tumor volumetric ICC were 0.997 and 0.996, respectively. Median [Interquartile range] DSC for eye, sclera, vitreous, lens, retinal detachment and tumor were 0.965 [0.950-0.975], 0.847 [0.782-0.893], 0.975 [0.930-0.986], 0.909 [0.847-0.951], 0.828 [0.458-0.962] and 0.914 [0.852-0.958], respectively. MV-CNN can be used to obtain accurate ocular structure and tumor segmentations in retinoblastoma.
Pubmed
Web of science
Open Access
Oui
Création de la notice
31/05/2021 9:07
Dernière modification de la notice
07/08/2021 6:37
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