Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning-Reference-Cited by-同舟云学术

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Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning

Published:2022-09-26 Issue:9 Volume:11 Page:25
ISSN:2164-2591
Container-title:Translational Vision Science & Technology
language:en
Short-container-title:Trans. Vis. Sci. Tech.

Author:

Valmaggia Philippe¹²³,Friedli Philipp⁴,Hörmann Beat⁴,Kaiser Pascal⁴,Scholl Hendrik P. N.²³,Cattin Philippe C.¹,Sandkühler Robin¹,Maloca Peter M.²³⁵

Affiliation:

1. Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

2. Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland

3. Department of Ophthalmology, University Hospital Basel, Basel, Switzerland

4. Supercomputing Systems AG, Zürich, Switzerland

5. Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

Ophthalmology,Biomedical Engineering

Reference46 articles.

1. Deep learning in medical image analysis;Shen;Annu Rev Biomed Eng,2017

2. Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the BraTS 2012-2018 Challenges;Ghaffari;IEEE Rev Biomed Eng,2020

3. Deep learning in ophthalmology: The technical and clinical considerations;Ting;Prog Retin Eye Res,2019

4. Optical coherence tomography;Huang;Science,1991

5. Validation of automated artificial intelligence segmentation of optical coherence tomography images;Maloca;PLoS One,2019

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