Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network

Tan, Jen Hong; Acharya, U. Rajendra; Bhandary, Sulatha V.; Chua, Kuang Chua; Sivaprasad, Sobha

doi:10.1016/j.jocs.2017.02.006

Cited by 221 publications

(94 citation statements)

References 54 publications

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“…In the field of ophthalmology, this study is the first to compare the performance of state-of-the-art object-detection architectures. There have been two major studies to date: one 21,22 involved object detection of the ONH in an entire fundus photograph, and the other 2,23,24 involved classification of the ONH in a cropped image. Object detection involves not only classifying every object in an image but also localizing them by drawing the appropriate bounding box.…”

Section: Discussionmentioning

confidence: 99%

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Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures

Park

Kim

Lee

2020

Sci Rep

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Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of ophthalmology. This study compared the performance of various state-of-the-art deep-learning architectures for detecting the optic nerve head and vertical cup-to-disc ratio in fundus images. Three different architectures were compared: YOLO V3, ResNet, and DenseNet. We compared various aspects of performance, which were not confined to the accuracy of detection but included, as well, the processing time, diagnostic performance, effect of the graphic processing unit (GPU), and image resolution. In general, as the input image resolution increased, the classification accuracy, localization error, and diagnostic performance all improved, but the optimal architecture differed depending on the resolution. The processing time was significantly accelerated with GPU assistance; even at the high resolution of 832 × 832, it was approximately 170 ms, which was at least 26 times slower without GPU. The choice of architecture may depend on the researcher's purpose when balancing between speed and accuracy. This study provides a guideline to determine deep learning architecture, optimal image resolution, and the appropriate hardware.Recently, there have been considerable advances in computer technology and breakthroughs in artificial-intelligence algorithms for "deep learning". In the field of ophthalmology, automated image analysis using deep learning is attracting increasing attention, and researchers have reported that it can outperform a human doctor 1,2 . One of the significant innovations in image-classification deep-learning algorithms was AlexNet 3 , which was developed by Alex Krizhevsky. AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 and was overwhelmingly victorious. This algorithm opened a new horizon of computer vision using deep convolutional neural networks (CNNs).Since AlexNet was developed, there have been numerous modifications to CNN architectures. One of these modifications-and another significant leap-was ResNet 4 , which is famous for its "residual connection. " Before ResNet, deep CNN architectures had a "degradation problem": with the increase of the depth of a neural network, the accuracy becomes saturated and decreases rapidly 5,6 . ResNet solved this problem via residual connection, which adds the output of the lower layer to the higher layer. This direct addition of the lower-layer output delivers undegraded information to the higher layer and makes deep neural network training more effective 4 . ResNet won 1 st place in the 2015 ILSVRC.Another state-of-the-art approach for solving the degradation problem is DenseNet, which was introduced by Huang et al. 7 in 2016. This architecture feed-forwards each layer to every other layer. ResNet also forwards the output of the lower layer directly to the higher layer; ho...

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Section: Discussionmentioning

confidence: 99%

“…However, although the cascade classifier performed well for high-quality images, the performance was significantly degraded for lower-quality images. Tan et al 22 reported segmentation of the ONH, fovea, and retinal vasculature using a single CNN. The localization accuracy (IoU) of segmenting the ONH was 0.6210.…”

Section: Discussionmentioning

confidence: 99%

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures

Park

Kim

Lee

2020

Sci Rep

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“…The DL algorithms have shown promising performance comparable to expert segmentation in fundus images [28,38]. The most unique advantage of DL is the ability to precisely learn and capture a huge amount of image features with varying hierarchies and locations.…”

Section: Research and Clinical Implicationsmentioning

confidence: 99%

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Islam

Poly

Walther

et al. 2020

JCM

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Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.

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“…These limitations can be overcome by utilizing some pre-processing steps in the future. Tan et al [49] implemented a seven-layer-based convolutional neural network (CNN) for the segmentation of retinal vessels and reported a classification accuracy of 94.54%. Wang et al [50] also used a CNN model with ensemble learning for vessel segmentation.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network

Cited by 221 publications

References 54 publications

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

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