Automatic Retinal Blood Vessel Segmentation Based on Multi-Level Convolutional Neural Network

Guo, Jinnan; Ren, Shiwei; Shi, Yueting; Wang, Haoyu

doi:10.1109/cisp-bmei.2018.8633115

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…Ngo and Han [151], Guo, et al [152], Li, et al [153] adopted multiple input branches to capture multi-scale spatial information. Further, all the feature maps generated by each branch are combined to make predictions.…”

Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

“…Further, all the feature maps generated by each branch are combined to make predictions. In addition, Guo, et al [152] applied the K-dimensions tree integrated with the hessian matrix to reconnect the broken segments in the postprocessing stage. Some broken vessels were reconnected and the vessel map became cleaner after post-processing.…”

Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

“…Li, et al [153] introduced sparse variables into the label design and improved the cross-entropy loss function to address the unbalance of samples. Li, et al [153] got better sensitivities than [151] and [152] because they solved the class imbalanced issue.…”

Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

“…The segmentation result contains noise and isolated small vessels, so we can use a matched filter or morphological transform to illuminate them in the post-processing phase [72,101]. The vessel segments are broken in some cases, and we can reconnect fractured vessels by some techniques, such as PRW and K-dimensions tree [95,152]. Better visualization of the vessel map helps ophthalmologists diagnose disease easier.…”

Section: B Key Aspects For Successful Retinal Vessel Segmentationmentioning

confidence: 99%

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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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Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

Section: F Other Network For Retinal Vessel Segmentationmentioning

confidence: 99%

Section: B Key Aspects For Successful Retinal Vessel Segmentationmentioning

confidence: 99%

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Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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“…Compared with U-NET, it can extract more tiny blood vessels, but at the cost of increased training and reasoning time [13]. To solve the problem of imbalanced pixels distribution in fundus images, a series of studies [14]- [16] focused on multi-scale feature extraction to achieve local feature extraction and global feature extraction, thereby achieve segment more tiny vessels. Yan et al [17] proposed a threestage network model to solve the imbalance between thick and thin vessels and the characteristic differences.…”

Section: Introductionmentioning

confidence: 99%

An Encoder-decoder Deep Learning Model Combining Mixed Attention Mechanism and Asymmetric Convolution for Automation of Retinal Vessels Segmentation

Cao¹,

Zhou²,

Chen³

et al. 2021

Preprint

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The segmentation of the retinal vascular tree is the fundamental step for diagnosing ophthalmological diseases and cardiovascular diseases. Most existing vessel segmentation methods based on deep learning give the learned features equal importance. Ignored the highly imbalanced ratio between background and vessels (the majority of vessel pixels belong to the background), the learned features would be dominantly guided by background, and relatively little influence comes from vessels, often leading to low model sensitivity and prediction accuracy. The reduction of model size is also a challenge. We propose a mixed attention mechanism and asymmetric convolution encoder-decoder structure(MAAC) for segmentation in Retinal Vessels to solve these problems. In MAAC, the mixed attention is designed to emphasize the valid features and suppress the invalid features. It not only identifies information that helps retinal vessels recognition but also locates the position of the vessel. All square convolutions are replaced by asymmetric convolutions because it is more robust to rotational distortions and small convolutions are more suitable for extracting vessel features (based on the thin characteristics of vessels). The employment of asymmetric convolution reduces model parameters and improve the recognition of thin vessel. The experiments on public datasets DRIVE, STARE, and CHASE\_DB1 demonstrated that the proposed MAAC could more accurately segment vessels with a global AUC of 98.17$\%$, 98.67$\%$, and 98.53$\%$, respectively. The mixed attention proposed in this study can be applied to other deep learning models for performance improvement without changing the network architectures. <br>

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Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

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Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

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Automatic Retinal Blood Vessel Segmentation Based on Multi-Level Convolutional Neural Network

Cited by 11 publications

References 19 publications

Retinal Vessel Segmentation Using Deep Learning: A Review

Retinal Vessel Segmentation Using Deep Learning: A Review

An Encoder-decoder Deep Learning Model Combining Mixed Attention Mechanism and Asymmetric Convolution for Automation of Retinal Vessels Segmentation

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

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