Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images

Biswas, Raj; Vasan, A. Keerthi; Roy, Sanjiban Sekhar

doi:10.1007/s40998-019-00213-7

Cited by 43 publications

(20 citation statements)

References 38 publications

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“…The separation of the training dataset into four different classes (capillaries, large vessel, image background, image canvas) is an exciting improvement on existing CNNs that have been used for vascular segmentation. Previous CNNs developed for and applied to fundus images have not differentiated between smaller or larger vessels, [26][27][28] likely due to the fact that fundus images do not possess the resolution necessary to visualize capillaries. A more recent CNN designed to segment capillaries in OCTA images was trained on images centered on the fovea (surrounding the foveal avascular zone), 29 where smaller vasculature is more prominent.…”

Section: Discussionmentioning

confidence: 99%

“…[22][23][24][25] However, translation of these thresholding algorithms to grayscale AOSLO perfusion images for the purposes of automatically segmenting retinal vasculature has proven challenging, primarily due to the large variations in contrast, brightness, and background signal that can typically manifest in AOSLO perfusion images. Machine learning techniques, such as convolutional neural networks (CNNs), have been developed for fundus [26][27][28] and OCTA 29 images. However, there is a lack of development in comparable techniques for AOSLO images.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Musial

Queener

Adhikari

et al. 2020

Trans. Vis. Sci. Tech.

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Adaptive optics scanning laser ophthalmoscope (AOSLO) capillary perfusion images can possess large variations in contrast, intensity, and background signal, thereby limiting the use of global or adaptive thresholding techniques for automatic segmentation. We sought to develop an automated approach to segment perfused capillaries in AOSLO images. Methods: 12,979 image patches were extracted from manually segmented AOSLO montages from 14 eyes and used to train a convolutional neural network (CNN) that classified pixels as capillaries, large vessels, background, or image canvas. 1764 patches were extracted from AOSLO montages of four separate subjects, and were segmented manually by two raters (ground truth) and automatically by the CNN, an Otsu's approach, and a Frangi approach. A modified Dice coefficient was created to account for slight spatial differences between the same manually and CNN-segmented capillaries. Results: CNN capillary segmentation had an accuracy (0.94), a Dice coefficient (0.67), and a modified Dice coefficient (0.90) that were significantly higher than other automated approaches (P < 0.05). There were no significant differences in capillary density and mean segment length between manual ground-truth and CNN segmentations (P > 0.05). Conclusions: Close agreement between the CNN and manual segmentations enables robust and objective quantification of perfused capillary metrics. The developed CNN is time and computationally efficient, and distinguishes capillaries from areas containing diffuse background signal and larger underlying vessels. Translational Relevance: This automatic segmentation algorithm greatly increases the efficiency of quantifying AOSLO capillary perfusion images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Musial

Queener

Adhikari

et al. 2020

Trans. Vis. Sci. Tech.

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“…Dilated convolution [97] was also introduced to retinal vessel segmentation to enlarge the receptive fields [98][99][100][101].…”

Section: U-net For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network.

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“…Literature [15] proposed a method for retinal vessel segmentation using patch-based fully convolutional networks. Literature [16] applied dilated convolutions in a deep neural network to improve the segmentation of retinal blood vessels from fundus images. Literature [17] proposed a new improved algorithm based on the U-Net network model.…”

Section: Biomed Research Internationalmentioning

confidence: 99%

Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

Zhu

Dong

et al. 2021

BioMed Research International

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Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

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Dilated Deep Neural Network for Segmentation of Retinal Blood Vessels in Fundus Images

Cited by 43 publications

References 38 publications

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Retinal Vessel Segmentation Using Deep Learning: A Review

Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

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