Retinal Vessel Segmentation Using Deep Learning: A Review

Chen, Chuansheng; Chuah, Joon Huang; Raza, Ali; Wang, Yizhou

doi:10.1109/access.2021.3102176

Cited by 78 publications

(51 citation statements)

References 149 publications

(202 reference statements)

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“…These results were comparable or superior to results of other deep learning-based approaches. 52 On the Automated Retinal Image Analysis external validation data set, the model achieved an accuracy score of 95.6% and an area under the curve of 97.4% higher than state-of-the-art image processingbased approaches (Figure S2). 51,[53][54][55][56]72,73 Although our model had a lower Dice coefficient in the Automated Retinal Image Analysis data set, (71.1%), FD and vessel density correlations remained high (ie, 0.93 and 0.82, respectively).…”

Section: Deep Learning For Automated Image Quality Control and Vessel...mentioning

confidence: 98%

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Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

et al. 2022

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Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification.

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Section: Deep Learning For Automated Image Quality Control and Vessel...mentioning

confidence: 98%

“…An overview of previous methods for retinal vessel segmentation 42,[51][52][53][54][55][56] is provided in the Supplemental Methods. vessel segmentations from the Structured Analysis of the Retina database 58…”

Section: Deep Learning For Vessel Segmentationmentioning

confidence: 99%

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

et al. 2022

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“…Many machine learning techniques have been developed in ophthalmology, including applications for the identification of retinal landmarks, retinal pathology segmentation, and retinal disease classification. As a result, reviews of deep learning works have been actively published, with some covering specific domains and some covering ophthalmology in general [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. A review of the applications in sub-domains has the advantage of providing detailed and rich content, but it can be difficult to note the importance of the applications in a large context, and a review may contain information that is not meaningful from an engineer’s point of view.…”

Section: Introductionmentioning

confidence: 99%

Review of Machine Learning Applications Using Retinal Fundus Images

2022

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Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

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“…Existing deep learning-based retinal vessel segmentation models can be classified into four groups according to network structure [ 8 ]. The first group is to use only a few layers of CNN to segment blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

Liu

Xiao

2022

Journal of Healthcare Engineering

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Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

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Retinal Vessel Segmentation Using Deep Learning: A Review

Cited by 78 publications

References 149 publications

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

Review of Machine Learning Applications Using Retinal Fundus Images

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

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