Literature DB >> 28285460

Recent Advancements in Retinal Vessel Segmentation.

Chetan L Srinidhi1, P Aparna2, Jeny Rajan3.   

Abstract

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

Entities:  

Keywords:  Fundus image; Retinal vessel; Review; Segmentation

Mesh:

Year:  2017        PMID: 28285460     DOI: 10.1007/s10916-017-0719-2

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  77 in total

1.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response.

Authors:  A Hoover; V Kouznetsova; M Goldbaum
Journal:  IEEE Trans Med Imaging       Date:  2000-03       Impact factor: 10.048

Review 2.  Familial retinal arteriolar tortuosity: a review.

Authors:  Florian K P Sutter; Horst Helbig
Journal:  Surv Ophthalmol       Date:  2003 May-Jun       Impact factor: 6.048

3.  Ridge-based vessel segmentation in color images of the retina.

Authors:  Joes Staal; Michael D Abràmoff; Meindert Niemeijer; Max A Viergever; Bram van Ginneken
Journal:  IEEE Trans Med Imaging       Date:  2004-04       Impact factor: 10.048

4.  A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features.

Authors:  Diego Marin; Arturo Aquino; Manuel Emilio Gegundez-Arias; José Manuel Bravo
Journal:  IEEE Trans Med Imaging       Date:  2010-08-09       Impact factor: 10.048

5.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification.

Authors:  João V B Soares; Jorge J G Leandro; Roberto M Cesar Júnior; Herbert F Jelinek; Michael J Cree
Journal:  IEEE Trans Med Imaging       Date:  2006-09       Impact factor: 10.048

6.  Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation.

Authors:  Roberto Annunziata; Andrea Garzelli; Lucia Ballerini; Alessandro Mecocci; Emanuele Trucco
Journal:  IEEE J Biomed Health Inform       Date:  2015-06-01       Impact factor: 5.772

7.  A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction.

Authors:  György Kovács; András Hajdu
Journal:  Med Image Anal       Date:  2015-12-19       Impact factor: 8.545

8.  Retinal arteriolar diameters and elevated blood pressure: the Atherosclerosis Risk in Communities Study.

Authors:  A R Sharrett; L D Hubbard; L S Cooper; P D Sorlie; R J Brothers; F J Nieto; J L Pinsky; R Klein
Journal:  Am J Epidemiol       Date:  1999-08-01       Impact factor: 4.897

9.  Computer-assisted image analysis of temporal retinal vessel width and tortuosity in retinopathy of prematurity for the assessment of disease severity and treatment outcome.

Authors:  Crystal S Y Cheung; Ziad Butty; Nasrin N Tehrani; Wai Ching Lam
Journal:  J AAPOS       Date:  2011-08       Impact factor: 1.220

10.  Tracing retinal vessel trees by transductive inference.

Authors:  Jaydeep De; Huiqi Li; Li Cheng
Journal:  BMC Bioinformatics       Date:  2014-01-18       Impact factor: 3.169

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  11 in total

1.  A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning.

Authors:  Pegah Kharazmi; Jiannan Zheng; Harvey Lui; Z Jane Wang; Tim K Lee
Journal:  J Med Syst       Date:  2018-01-09       Impact factor: 4.460

2.  Segmentation of retinal blood vessels based on feature-oriented dictionary learning and sparse coding using ensemble classification approach.

Authors:  Navdeep Singh; Lakhwinder Kaur; Kuldeep Singh
Journal:  J Med Imaging (Bellingham)       Date:  2019-11-22

3.  "Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging.

Authors:  Weilin Fu; Katharina Breininger; Roman Schaffert; Zhaoya Pan; Andreas Maier
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-04-30       Impact factor: 2.924

4.  Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography via Iterative Multi-Modal Registration and Learning.

Authors:  Li Ding; Ajay E Kuriyan; Rajeev S Ramchandran; Charles C Wykoff; Gaurav Sharma
Journal:  IEEE Trans Med Imaging       Date:  2021-09-30       Impact factor: 11.037

5.  Augmented reality guidance in cerebrovascular surgery using microscopic video enhancement.

Authors:  Reid Vassallo; Hidetoshi Kasuya; Benjamin W Y Lo; Terry Peters; Yiming Xiao
Journal:  Healthc Technol Lett       Date:  2018-09-18

6.  Determining blood flow direction from short neurovascular surgical microscope videos.

Authors:  Reid Vassallo; Adam Rankin; Stephen P Lownie; Hitoshi Fukuda; Hidetoshi Kasuya; Benjamin W Y Lo; Terry Peters; Yiming Xiao
Journal:  Healthc Technol Lett       Date:  2019-11-26

7.  Self-relabeling for noise-tolerant retina vessel segmentation through label reliability estimation.

Authors:  Jiacheng Li; Ruirui Li; Ruize Han; Song Wang
Journal:  BMC Med Imaging       Date:  2022-01-12       Impact factor: 1.930

8.  Retinal Imaging Techniques Based on Machine Learning Models in Recognition and Prediction of Mild Cognitive Impairment.

Authors:  Qian Zhang; Jun Li; Minjie Bian; Qin He; Yuxian Shen; Yue Lan; Dongfeng Huang
Journal:  Neuropsychiatr Dis Treat       Date:  2021-11-06       Impact factor: 2.570

9.  A Few-Shot Learning-Based Retinal Vessel Segmentation Method for Assisting in the Central Serous Chorioretinopathy Laser Surgery.

Authors:  Jianguo Xu; Jianxin Shen; Cheng Wan; Qin Jiang; Zhipeng Yan; Weihua Yang
Journal:  Front Med (Lausanne)       Date:  2022-03-03

10.  Microvasculature Segmentation and Intercapillary Area Quantification of the Deep Vascular Complex Using Transfer Learning.

Authors:  Julian Lo; Morgan Heisler; Vinicius Vanzan; Sonja Karst; Ivana Zadro Matovinović; Sven Lončarić; Eduardo V Navajas; Mirza Faisal Beg; Marinko V Šarunić
Journal:  Transl Vis Sci Technol       Date:  2020-07-10       Impact factor: 3.283

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