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Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network [article]

Donghuan Lu, Morgan Heisler, Sieun Lee, Gavin Ding, Marinko V. Sarunic, Mirza Faisal Beg
2017 arXiv   pre-print
Based on the raw images and layers segmented by a graph-cut algorithm, a fully convolutional neural network was trained to recognize and label the fluid pixels.  ...  As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures.  ...  Brain Canada Foundation, Alzheimer Society of Canada, the Pacific Alzheimer Research Foundation, Genome British Columbia, and the Michael Smith Foundation for Health Research (MSFHR).  ... 
arXiv:1710.04778v1 fatcat:5f6vekxklnepvnol2va2rndnpi

Deep-learning based, automated segmentation of macular edema in optical coherence tomography

Cecilia S. Lee, Ariel J. Tyring, Nicolaas P. Deruyter, Yue Wu, Ariel Rokem, Aaron Y. Lee
2017 Biomedical Optics Express  
We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians.  ...  In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions.  ...  Additionally, deep convolutional neural networks (CNN) have facilitated breakthroughs in image processing and segmentation [1].  ... 
doi:10.1364/boe.8.003440 pmid:28717579 pmcid:PMC5508840 fatcat:nmgzm3vt3rhdjcaodzivh6hezm

Index [chapter]

2021 State of the Art in Neural Networks and their Applications  
See Spectral domain optical coherence tomography (SD-OCT) Full Field Digital Mammograms (FFDMs), 159À160 Fully connected layers (FC layers), 79, 157 Fully convolutional network (FCN), 65, 157, 19À20 Peak  ...  tumor segmentation from MRI, 284 optical coherence tomography, 244, 245t Decision tree (DT), 22 Deep convolutional neural network (DCNN), 93 architectures of, 4À6 renal transplant classification  ... 
doi:10.1016/b978-0-12-819740-0.00023-1 fatcat:dz4hretj2fbh5cmevwejvz56zy

Deep-Learning Based, Automated Segmentation Of Macular Edema In Optical Coherence Tomography [article]

Cecilia S. Lee, Ariel J. Tyring, Nicolaas P. Deruyter, Yue Wu, Ariel Rokem, Aaron Y. Lee
2017 bioRxiv   pre-print
We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians.  ...  In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions.  ...  Additionally, deep convolutional neural networks (CNN) have facilitated breakthroughs in image processing and segmentation [1].  ... 
doi:10.1101/135640 fatcat:c6quepy55zc6vpl57s2weizae4

Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs [article]

Fangliang Bai, Manuel J. Marques, Stuart J. Gibson
2017 arXiv   pre-print
In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected  ...  As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME regions using the trained model.  ...  and hardware deployment from Nicholas French (SPS, Kent).  ... 
arXiv:1709.05324v1 fatcat:5qpn65gvdvclha2dtith7q6bii

Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images

2019 International Journal of Ophthalmology  
In order to solve the category imbalance in retinal optical coherence tomography (OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified.  ...  Thus, we proposed a three-dimensional (3D) fully convolutional network for segmentation in the retinal OCT images.  ...  Optical coherence tomography (OCT) technology, as a rapidly emerging type of medical imaging technology, offers various advantages and broad application prospects.  ... 
doi:10.18240/ijo.2019.06.22 pmid:31236362 pmcid:PMC6580226 fatcat:peixwdzd4fha7ijj7uygeg67wy

Image Segmentation Techniques for Healthcare Systems

Orazio Gambino, Vincenzo Conti, Sergio Galdino, Cesare Fabio Valenti, Wellington Pinheiro dos Santos
2019 Journal of Healthcare Engineering  
Khagi and G.-R. Kwon proposed a method to segment MR brain images using SegNet, a convolutional neural network. Such a CNN is trained by using presegmented MR brain images of the OASIS free dataset.  ...  Kong et al. propose a method to segment MR brain images by means of a convolutional neural network.  ...  Acknowledgments e lead guest editor and his editorial staff express their gratefulness both to all the reviewers for their precious support and to the authors who decided to publish their works in this  ... 
doi:10.1155/2019/2723419 pmid:31065330 pmcid:PMC6466904 fatcat:swam5ctrsre3fk55f4ezrn2uuy

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.  ...  fully convolutional network, termed LF-UNet.  ...  Introduction Optical coherence tomography (OCT) has been widely used to detect and monitor pathologies from retinal diseases.  ... 
arXiv:1912.03418v1 fatcat:onnfsookjzaobjmm6eo4sa3dgm

Optical coherence tomography image for automatic classification of diabetic macular edema

Ping Wang, Jia-Li Li, Hao Ding
2020 Optica Applicata  
Based on transfer learning, an automatic classification method is proposed to distinguish DME images from normal images in optical coherence tomography (OCT) retinal fundus images.  ...  Features of the DME are automatically identified and extracted by the pre-trained convolutional neural network (CNN), which only involves fine-tuning the VGGNet-16 network without any user intervention  ...  In this paper, an automatic diagnostic system by fine-tuning the VGGNet-16 network is proposed to distinguish DME images from optical coherence tomography (OCT) retinal fundus normal images.  ... 
doi:10.37190/oa200405 fatcat:mynw6ogssvenfprxyynjx5xlrq

Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning [article]

Yukun Guo, Tristan T. Hormel, Honglian Xiong, Jie Wang, Thomas S. Hwang, Yali Jia
2020 arXiv   pre-print
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume.  ...  A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net.  ...  Bai et.al. used a fully convolutional neural network (CNN) and a fully connected conditional random field method to segment cystoid macular edema 24 .  ... 
arXiv:2006.02569v1 fatcat:wffbe25kbbccrhegpn4fizdb7q

Deep Learning based Retinal OCT Segmentation [article]

Mike Pekala, Neil Joshi, David E. Freund, Neil M. Bressler, Delia Cabrera DeBuc, Philippe M Burlina
2018 arXiv   pre-print
Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the retina.  ...  The proposed automated approach segments images using fully convolutional networks (FCNs) together with Gaussian process (GP)-based regression as a post-processing step to improve the quality of the estimates  ...  Acknowledgments This work is supported by the JHU/APL Independent Research and Development Program. We thank Dr. Jun Kong for interesting discussions on OCT.  ... 
arXiv:1801.09749v1 fatcat:474syzsyp5cxfljj646ecbtq6m

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 CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net.  ...  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

OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Oscar Perdomo, Sebastian Otalora, Fabio A. Gonzalez, Fabrice Meriaudeau, Henning Muller
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
Optical Coherence Tomography (OCT) volumes have been widely used to diagnose different eye diseases, thanks to their sensitivity to represent small amounts of fluid, thickness between layers and swelling  ...  Convolutional neural networks (CNNs) have shown outstanding performance when applied to several medical images analysis tasks.  ...  Lee et al. developed an automated segmentation based on convolutional neural networks (CNN) that detect DME [9] and Age-related macular degeneration (AMD) [10] on OCT volumes.  ... 
doi:10.1109/isbi.2018.8363839 dblp:conf/isbi/PerdomoOGMM18 fatcat:udsyzfeqiballialobkr2s3hta

Machine Learning Techniques for Ophthalmic Data Processing: A Review

Mhd Hasan Sarhan, Mohammad Ali Nasseri, Daniel Zapp, Mathias Maier, Chris Lohmann, Nassir Navab, Abouzar Eslami
2020 IEEE journal of biomedical and health informatics  
Two main imaging modalities are considered in this survey, namely color fundus imaging, and optical coherence tomography.  ...  Furthermore, the recent machine learning approaches used for retinal vessels segmentation, and methods of retinal layers and fluid segmentation are reviewed.  ...  , and various abnormalities related to ophthalmic diseases. (2) Optical Coherence Tomography (OCT) imaging is used to acquire high-resolution cross-sectional scans of the retinal layers in the eye's posterior  ... 
doi:10.1109/jbhi.2020.3012134 pmid:32750971 fatcat:f4mmjk2ferduzbkpza4hoizzjq

Automatic Segmentation of Retinal Fluid and Photoreceptor Layer from Optical Coherence Tomography Images of Diabetic Macular Edema Patients Using Deep Learning and Associations with Visual Acuity

Huan-Yu Hsu, Yu-Bai Chou, Ying-Chun Jheng, Zih-Kai Kao, Hsin-Yi Huang, Hung-Ruei Chen, De-Kuang Hwang, Shih-Jen Chen, Shih-Hwa Chiou, Yu-Te Wu
2022 Biomedicines  
Optical coherence tomography (OCT) is crucial in classifying DME and tracking the results of DME treatment.  ...  However, the manual segmentation of retinal fluid and the EZ from retinal OCT images is laborious and time-consuming.  ...  In clinical practice, optical coherence tomography (OCT) has been widely used to detect DME and classify its components [2, 3] .  ... 
doi:10.3390/biomedicines10061269 pmid:35740291 fatcat:5jd62olzp5cfzpsir32wb36jt4
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