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

Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H.; Li, Shutao; Farsiu, Sina

doi:10.1364/boe.8.002732

Cited by 429 publications

(289 citation statements)

References 59 publications

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“…Numerous approaches to solve the problem of delineating layer boundaries in OCT of pathological retinas have been developed successfully, encompassing methods based on directional graph search [30], machine learning and auto-context [39], kernel regression [40] and deep learning [41]. Some pathologies that might appear in advanced stages of DR such as subretinal or intra-retinal fluid pose segmentation challenges in B-scans due to their irregular and unpredictable shapes, but not due to insufficient contrast with surrounding tissue.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

Wang¹,

Camino²,

Zhang³

et al. 2017

Biomed. Opt. Express

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Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.

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Section: Discussionmentioning

confidence: 99%

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

Wang¹,

Camino²,

Zhang³

et al. 2017

Biomed. Opt. Express

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show abstract

“…In addition, semiautomated methods included artifact with segmentations [19], inaccurately detected microcysts [20], and could not distinguish cystic from non-cystic fluid accumulations [21]. CNN was recently used for automated segmentation of the anatomic retinal boundaries by Fang et al, however, it has not yet been applied for automated segmentation of IRF [22]. Additionally, we demonstrated that deep learning is effective in classifying OCT images of patients with age-related macular degeneration [23].…”

Section: Introductionmentioning

confidence: 86%

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

Lee

Tyring

Deruyter

et al. 2017

Biomed. Opt. Express

296

165

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Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts. Additionally, the agreement between experts and between experts and CNN were similar. Our results reveal that CNN can be trained to perform automated segmentations of clinically relevant image features.

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“…Many studies successfully used CNN to segment the target region using CT and MRI for various organs, such as the brain, liver, kidney, and prostate [56][57][58][59][60][61]. Fang et al [62] proposed an automatic segmentation algorithm of retina layer boundaries using CNN for high-resolution optical coherence tomography imaging. Xu et al [63] adopted CNN architecture for segmenting epithelial and stromal regions in histology images.…”

Section: Image Processing Applications Using Cnn Architecturementioning

confidence: 99%

Prospects of deep learning for medical imaging

2018

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Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. 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. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.

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

Cited by 429 publications

References 59 publications

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

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

Prospects of deep learning for medical imaging

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