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Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence [article]

Donghuan Lu, Morgan Heisler, Da Ma, Setareh Dabiri, Sieun Lee, Gavin Weiguang Ding, Marinko V. Sarunic, Mirza Faisal Beg
2019 arXiv   pre-print
In this paper, a novel framework was proposed to segment retinal layers with fluid presence.  ...  Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.  ...  Introduction Optical coherence tomography (OCT) has been widely used to detect and monitor pathologies from retinal diseases.  ... 
arXiv:1912.03418v1 fatcat:onnfsookjzaobjmm6eo4sa3dgm

Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning

Yukun Guo, Tristan T. Hormel, Honglian Xiong, Jie Wang, Thomas S. Hwang, Yali Jia
2020 Translational Vision Science & Technology  
We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT)  ...  A deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation.  ...  Bai et al. 29 use a fully convolutional neural network (CNN) and a fully connected conditional random field method to segment cystoid macular edema.  ... 
doi:10.1167/tvst.9.2.54 pmid:33110708 pmcid:PMC7552937 fatcat:awntadmbbbajrb4qhy6dookg2u

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim
2021 Medical and Biological Engineering and Computing  
Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases.  ...  The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical  ...  Ik Hee Ryu and VISUWORKS, Inc., which is a Korean AI startup providing medical machine learning solutions.  ... 
doi:10.1007/s11517-021-02321-1 pmid:33492598 fatcat:xjqxmqalg5dz5kuonflzqj2spe

Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis [article]

Mrinal Haloi
2018 arXiv   pre-print
The proposed system is a very deep fully convolutional attentive classification network trained with end to end advanced transfer learning with online random augmentation.  ...  In this work, we propose an advanced AI based grading system for OCT images.  ...  A deep learning based OCT image segmentation method [12] proposed pathological lesions segmentation using a fully convolutional method.  ... 
arXiv:1812.07105v1 fatcat:ve2l72cqsbfglk3gahrcgyai2y

Deep learning-based automated detection of retinal diseases using optical coherence tomography images

Feng Li, Hua Chen, Zheng Liu, Xue-dian Zhang, Min-shan Jiang, Zhi-zheng Wu, Kai-qian Zhou
2019 Biomedical Optics Express  
This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT  ...  The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50  ...  Acknowledgements The authors acknowledge the Shanghai Zhongshan Hospital and the Shanghai First People's Hospital for our help and support.  ... 
doi:10.1364/boe.10.006204 pmid:31853395 pmcid:PMC6913386 fatcat:xbe5yvohknbk7kunlmia7iipuq

Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach [article]

Misgina Tsighe Hagos
2020 arXiv   pre-print
Deep learning and mobile application development have been integrated in this dissertation to provide an easy to use point-of-care smartphone based diagnosis of diabetic retinopathy.  ...  Methods to exploit deep learning applications in healthcare have been proposed and implemented in this dissertation.  ...  And in deep learning based detection works deep and narrow neural networks have been extensively used.  ... 
arXiv:2002.04066v1 fatcat:evsa2qfaebcy5jfjgcespdbhoi

Automated Quantification of Pathological Fluids in Neovascular Age-Related Macular Degeneration, and Its Repeatability Using Deep Learning

Irmela Mantel, Agata Mosinska, Ciara Bergin, Maria Sole Polito, Jacopo Guidotti, Stefanos Apostolopoulos, Carlos Ciller, Sandro De Zanet
2021 Translational Vision Science & Technology  
(IRF), subretinal fluid (SRF), and pigment epithelium detachment (PED), using a deep-learning approach.  ...  One hundred seven spectral domain optical coherence tomography (OCT) cube volumes were extracted from nAMD eyes. Manual annotation of IRF, SRF, and PED was performed.  ...  Acknowledgments Supported by a research grant from a fund dedicated for research in age-related macular degeneration.  ... 
doi:10.1167/tvst.10.4.17 pmid:34003996 pmcid:PMC8083067 fatcat:ankcq25fprf5jovxm3siw2lie4

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis – A Review [article]

Sourya Sengupta, Amitojdeep Singh, Henry A.Leopold, Tanmay Gulati, Vasudevan Lakshminarayanan
2019 arXiv   pre-print
Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed.  ...  An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented.  ...  He used a 5 layer pixel based deep neural network to detect MA in MESSIDOR (Section 2.1.15) and ROC (Section 2.1.22).  ... 
arXiv:1812.07101v3 fatcat:weoh4wnw4ngy5mmq7vwgr2p77e

Ocular Disease Detection Using Advanced Neural Network Based Classification Algorithms

Nadim Mahmud Dipu, Sifatul Alam Shohan, K.M.A Salam
2021 Asian journal of convergence in technology  
In this study, we present four deep learning-based models for targeted ocular tumor detection.  ...  That is why a computer-aided automated ocular disease detection system is required for the early detection of various ocular diseases using fundus images.  ...  [15] suggested a fully convolutional deep architecture called ReLayNet for segmenting retinal layers and fluids from Optical Coherence Tomography (OCT) scans.  ... 
doi:10.33130/ajct.2021v07i02.019 fatcat:ucoaqy3545gfvdsfakagx7mw5y

Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

Gabriella Moraes, Dun Jack Fu, Marc Wilson, Hagar Khalid, Siegfried K. Wagner, Edward Korot, Daniel Ferraz, Livia Faes, Christopher J. Kelly, Terry Spitz, Praveen J. Patel, Konstantinos Balaskas (+3 others)
2020 Ophthalmology (Rochester, Minn.)  
A deep learning algorithm was used to segment all baseline OCT scans.  ...  To apply a deep learning algorithm for automated, objective, and comprehensive quantification of optical coherence tomography (OCT) scans to a large real-world dataset of eyes with neovascular age-related  ...  Deep-learning based multiclass retinal fluid segmentation 554 and detection in optical coherence tomography images using a fully convolutional 555 neural network.  ... 
doi:10.1016/j.ophtha.2020.09.025 pmid:32980396 pmcid:PMC8528155 fatcat:qla6dgyo5ja3vczlyt6uk5fpru

3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen [article]

Michaël J.A. Girard, Satish K. Panda, Tin Aung Tun, Elisabeth A. Wibroe, Raymond P. Najjar, Aung Tin, Alexandre H. Thiéry, Steffen Hamann, Clare Fraser, Dan Milea
2021 arXiv   pre-print
Purpose: (1) To develop a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography (OCT) scans; (2) to exploit such information to robustly  ...  At first, a deep learning algorithm was developed using 984 B-scans (from 130 eyes) in order to identify: major neural/connective tissues, and ODD regions.  ...  in Pytorch. [22] Unet++ is a fully convolutional neural network for semantic segmentation consisting of an encoder, a decoder, and a series of "nested, dense skip pathways", with a performance superior  ... 
arXiv:2112.09970v1 fatcat:lewc7mcygrb5xbeavt7jeldgka

Prospects of deep learning for medical imaging

Jonghoon Kim, Jisu Hong, Hyunjin Park
2018 Precision and Future Medicine  
This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research.  ...  First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given.  ...  In histology, , computer-aided diagnosis; CADe, computer-aided detection; MRI, magnetic resonance imaging; CT, computed tomography; PET, positron emission tomography; OCT, optical coherence tomography  ... 
doi:10.23838/pfm.2018.00030 fatcat:2bclzigfijadzcdoqhhzniqwdy

Artificial intelligence and deep learning in ophthalmology

Daniel Shu Wei Ting, Louis R Pasquale, Lily Peng, John Peter Campbell, Aaron Y Lee, Rajiv Raman, Gavin Siew Wei Tan, Leopold Schmetterer, Pearse A Keane, Tien Yin Wong
2018 British Journal of Ophthalmology  
In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy  ...  Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years.  ...  have been trained to segment intraretinal fluid cysts and subretinal fluid on OCT B-scans. 13 37 38 Deep convolutional networks surpassed traditional methods in the quality of segmentation of retinal  ... 
doi:10.1136/bjophthalmol-2018-313173 pmid:30361278 pmcid:PMC6362807 fatcat:xggqvj4bevegfdrjvdap6ibp3m

Convolutional mixture of experts model: A comparative study on automatic macular diagnosis in retinal optical coherence tomography imaging

Alireza Mehridehnavi, Reza Rasti, Hossein Rabbani, Fedra Hajizadeh
2019 Journal of Medical Signals & Sensors  
This allows having a fast and robust computer-aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal  ...  For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional  ...  [12] Regular convolutional neural networks CNN is a deep neural network model that captures spatial information of the input image data.  ... 
doi:10.4103/jmss.jmss_27_17 pmid:30967985 pmcid:PMC6419560 fatcat:m3tmzdncvveyfpusoi2kkzqrd4

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Md Shakib Khan, Nafisa Tafshir, Kazi Nabiul Alam, Abdur Rab Dhruba, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki, Muhammad Zubair Asghar
2022 Computational Intelligence and Neuroscience  
A deep-learning-based approach to targeted ocular detection is presented in this study.  ...  Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities.  ...  [25] proposed ReLayNet, a fully convolutional deep network for segmenting retinal layers and fluids from OCT scans. is technique uses an encoder-decoder network to segment semantic information from  ... 
doi:10.1155/2022/5007111 pmid:35528343 pmcid:PMC9071974 fatcat:imqbqfexfnbchcvhga5zcbusaa
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