Retinal Blood Vessel Segmentation: A Semi-supervised Approach

Ghosh, Tanmai K.; Saha, Sajib; Rahaman, G. M. Atiqur; Sayed, Md. Abu; Kanagasingam, Yogesan

doi:10.1007/978-3-030-31321-0_9

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The proposed framework's remarkable accuracy rate and ability to utilize both labelled and unlabelled data make it a promising solution for retinal vessel segmentation in the medical realm [13].…”

Section: Applications Of Ai In Detecting Abnormalities and Diseases I...mentioning

confidence: 99%

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

2023

AJBSR

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The application of new techniques in eye imaging has revolutionized the process by increasing accuracy and efficiency levels considerably. An essential aspect of analyzing ocular images is the correct segmentation of retinal vessels. Deep learning algorithms represent an effective solution giving high accuracy rates for this task. In this article readers can find an extensive overview of several novel approaches built specifically to address this issue-among them being a groundbreaking deep learning architecture that applies multi-scale/multi-level Convolutional Neural Networks (CNNs) to obtain a comprehensive hierarchical representation of retinal vessels. Furthermore, advanced approaches are also discussed on detecting retinal vessels from Fluorescein Angiography (FA) images -with notable examples being one that combines cross modality transfer with human in the loop learning helping reduce manual labelling efforts significantly while enhancing both accuracy and performance metrics simultaneously. These stateof-the-art methods unravel critical insights into how deep learning methods could improve retinal vessel segmentation. The impact they could have on diagnosing and treating various eye related illnesses is also noteworthy.

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Section: Applications Of Ai In Detecting Abnormalities and Diseases I...mentioning

confidence: 99%

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

2023

AJBSR

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“…In automated detection of ophthalmic diseases, retinal blood vessels are typically segmented and eliminated in the pre-processing stage [1] so that it does not interfere with pathologies. Blood vessels are considered more reliable than other features when it comes to registering retinal images collected from different viewpoints or at different sites [2][3][4][5][6]. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels, such as length, width, tortuosity and/or branching pattern and angles are utilized for diagnosis, screening, treatment and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis and choroidal neovascularization [7].…”

Section: Introductionmentioning

confidence: 99%

An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods

Sayed

Saha

Rahaman

et al. 2020

IET Image Processing

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Segmentation of retinal blood vessels is a very important diagnostic procedure in ophthalmology. Segmenting blood vessels in the presence of pathological lesions is a major challenge. In this paper, an innovative approach to segment the retinal blood vessel in the presence of pathology is proposed. The method combines both supervised and unsupervised approaches in the retinal imaging context. Two innovative descriptors named local Haar pattern and modified speeded up robust features are also proposed. Experiments are conducted on three publicly available datasets named: DRIVE, STARE and CHASE DB1, and the proposed method has been compared against the state-of-the-art methods. The proposed method is found about 1% more accurate than the best performing supervised method and 2% more accurate than the state-of-the-art Nguyen et al.'s method.

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Retinal Blood Vessel Segmentation: A Semi-supervised Approach

Cited by 2 publications

References 14 publications

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods

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