Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks

Sui, Xiaodan; Zheng, Yuanjie; Wei, Benzheng; Bi, Hongsheng; Wu, Jianfeng; Pan, Xuemei; Yin, Yilong; Zhang, Shaoting

doi:10.1016/j.neucom.2017.01.023

Cited by 122 publications

(69 citation statements)

References 40 publications

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“…conditional random fields, Markov random fields, and graph cut) for specific applications [47][48][49][50][51]. Most recently (after the conference version of our paper was accepted for presentation at the 2017 ARVO conference), a related method based on multiscale convolutional neural networks combined with graph search was published for segmenting the choroid in OCT retinal images [51]. Our paper is in the same class of segmentation algorithms, which combines the CNN model with graph search methodology (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on human retinal OCT images.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

Fang¹,

Cunefare²,

Wang³

et al. 2017

Biomed. Opt. Express

443

283

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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. 1178-1181 (1991). 2. S. Bhat, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, "4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences," IEEE Trans. Med. Imaging 32(3), 578-588 (2013). 3. P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, "Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration," Invest. Ophthalmol. Vis. Sci. 50(7), 3378-3385 (2009). 4. P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, "Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative agerelated macular degeneration," Retina 31(3), 453-463 (2011). 5. P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images," Biomed. Opt. Express 5(10), 3568-3577 (2014). 6. J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W.Gardner, "The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function," Invest. Ophthalmol. Vis. Sci. 57(1), 208-217 (2016). 7. C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G.Fujimoto, "Imaging of macular diseases with optical coherence tomography," Ophthalmology 102(2), 217-229 (1995

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Section: Introductionmentioning

confidence: 99%

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

Fang¹,

Cunefare²,

Wang³

et al. 2017

Biomed. Opt. Express

443

283

View full text Add to dashboard Cite

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Section: Topological Structurementioning

confidence: 99%

“…After two-dimensional topological transformation (stretching and compression) of the rectangle, the length, width and height of the rectangle all change, and the ratio also changes. Thus, this built-in property that point X is inside the rectangle, is called a topological property or a topological feature [29][30][31][32]. Topological structure plays an important role in image segmentation and classification, but it is not widely used in data clustering, which makes it a research blank for combining topological structure information with clustering algorithm.…”

Section: Topological Structurementioning

confidence: 99%

Density Peak Clustering Algorithm Considering Topological Features

Zheng

Luo

et al. 2020

Electronics

Self Cite

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The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.Electronics 2020, 9, 459 2 of 16The K-means algorithm is an algorithm that adopts the alternative minimization method to solve non-convex optimization problems [11,12] and it is a representative of the prototype-based clustering method of objective functions. It divides a given data set into K clusters designated by users and has a high execution efficiency. However, the K-means algorithm needs to specify the number of clustering centers in advance. Different K values have a great impact on the experimental results and, in the actual operation, multiple attempts are required to obtain the optimal K value. Firstly, the objectivity of the K value makes the clustering results mostly different. Secondly, this algorithm is not friendly to non-convex data sets and it is difficult to obtain satisfactory clustering results.The AP clustering algorithm [13,14] is an unsupervised clustering algorithm. The algorithm realizes the clustering process through the interaction between data points. The advantages of the algorithm are that there is no need to define the number of classes in advance, the clustering center can be obtained adaptively through the iterative process, and the location and number of class centers can be identified automatically from the data points, so as to maximize the similarity sum of all similar points. At the same time, the algorithm has disadvantages. Since the AP algorithm needs to calculate the similarity between each pair of data objects in advance, when there are many data objects, the memory footprint will be high. Moreover, its high time complexity O(N3) is one of its shortcomings.The DBSCAN algorithm [15,16] is a clustering algorithm based on data point density. It gets rid of the constraint o...

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“…These machine learning and deep learning approaches pick the specific characteristics from the training dataset, and then either apply layer segmentation directly on the validation dataset, or supervise the testing procedure in a teacher‐student model to improve iteratively the classifier model. Early investigations include support vector machine, random forest, and neural network, and more recently, convolutional neural networks, such as U‐Net and ReLayNet, and have been used for retinal layer segmentations in healthy subjects and age‐related macular degeneration patients …”

Section: Segmentation Of Oct‐amentioning

confidence: 99%

Future clinical applicability of optical coherence tomography angiography

Chua

Tan

Ang

et al. 2019

Clinical and Experimental Optometry

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Optical coherence tomography angiography (OCT-A) is an emerging technology that allows for the non-invasive imaging of the ocular microvasculature. Despite the wealth of observations and numerous research studies illustrating the potential clinical uses of OCT-A, this technique is currently rarely used in routine clinical settings. In this review, technical and clinical aspects of OCT-A imaging are discussed, and the future clinical potential of OCT-A is considered. An understanding of the basic principles and limitations of OCT-A technology will better inform clinicians of its future potential in the diagnosis and management of ocular diseases.

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Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks

Cited by 122 publications

References 40 publications

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

Density Peak Clustering Algorithm Considering Topological Features

Future clinical applicability of optical coherence tomography angiography

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