Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.
TLDR
A novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images is presented.Abstract:
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.read more
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Journal ArticleDOI
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw,Joseph R. Ledsam,Bernardino Romera-Paredes,Stanislav Nikolov,Nenad Tomasev,Sam Blackwell,Harry Askham,Xavier Glorot,Brendan O'Donoghue,Daniel Visentin,George van den Driessche,Balaji Lakshminarayanan,Clemens Meyer,Faith Mackinder,Simon Bouton,Kareem Ayoub,Reena Chopra,Dominic King,Alan Karthikesalingam,Cian Hughes,Rosalind Raine,Julian Hughes,Dawn A Sim,Catherine A Egan,Adnan Tufail,Hugh Montgomery,Demis Hassabis,Geraint Rees,Trevor Back,Peng T. Khaw,Mustafa Suleyman,Julien Cornebise,Pearse A. Keane,Olaf Ronneberger +33 more
TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
Journal ArticleDOI
Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting,Louis R. Pasquale,Lily Peng,John P. Campbell,Aaron Y. Lee,Rajiv Raman,Gavin Tan,Leopold Schmetterer,Pearse A. Keane,Tien Yin Wong +9 more
TL;DR: There are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms.
Journal ArticleDOI
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.
Abhijit Guha Roy,Sailesh Conjeti,Sri Phani Krishna Karri,Debdoot Sheet,Amin Katouzian,Christian Wachinger,Nassir Navab +6 more
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Journal ArticleDOI
Deep-learning based, automated segmentation of macular edema in optical coherence tomography
TL;DR: A convolutional neural network (CNN) is developed that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians and can be trained to perform automated segmentations of clinically relevant image features.
Journal ArticleDOI
Retinal Lesion Detection With Deep Learning Using Image Patches.
TL;DR: In this article, a sliding window method was used to generate probability maps across the entire image, which were used to detect and distinguish subtle pathologic lesions with only a few hundred training examples per lesion.
References
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