Retinal blood vessel segmentation using fully convolutional network with transfer learning

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Zhexin JiangSeok-Bum Ko

Abstract

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging. Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. Extensive experiments have been conducted on DRIVE, STARE, CHASE_DB1 and HRF databases, and the accuracy of the cross-database test on these four databases is state-of-the-art, which also presents the high robustness of the proposed approach. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging.

Citations

May 16, 2020·Current Medical Imaging·Muhammad Nadeem AshrafZulfiqar Habib
Mar 25, 2019·Medical & Biological Engineering & Computing·Weihua WangZhangping Hu
Dec 17, 2019·Journal of X-ray Science and Technology·Tiejun YangQi Tang
Jul 26, 2019·Journal of Digital Imaging·Sathananthavathi VSwetha Ranjani A
Sep 15, 2020·Biomedical Optics Express·Varun BelagaliPrasanta Kumar Ghosh
Apr 9, 2020·Journal of Clinical Medicine·Md Mohaimenul IslamYu-Chuan Jack Li
Feb 20, 2020·BMC Medical Imaging·Yanfei Guo, Yanjun Peng
Oct 2, 2019·Journal of Medical Imaging·Kejuan YueQing Liu
Oct 12, 2020·Computer Methods and Programs in Biomedicine·Pearl Mary Samuel, Thanikaiselvan Veeramalai
Jan 2, 2021·Medical Image Analysis·Muthu Rama Krishnan MookiahEmanuele Trucco
Feb 26, 2021·Computer Methods and Programs in Biomedicine·UNKNOWN INSPIRED project
Apr 13, 2021·Journal of Medical Engineering & Technology·Dhimas Arief Dharmawan
Aug 29, 2020·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Jinzhu YangChaolu Feng
Apr 22, 2021·Computer Methods and Programs in Biomedicine·Manuel E Gegundez-AriasManuel J Vasallo-Vazquez
Apr 24, 2021·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Henda BoudeggaAsma Ben Abdallah
Jun 5, 2021·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Riel Castro-ZuntiSeok-Bum Ko
Jun 29, 2021·Frontiers in Cell and Developmental Biology·Jingfei HuJicong Zhang
Jun 20, 2021·Computer Methods and Programs in Biomedicine·Zefang LinXiong Liang
Aug 7, 2021·Medical & Biological Engineering & Computing·Thanh-Tu PhamEdmond Lou
Aug 21, 2021·Artificial Intelligence in Medicine·José MoranoJosé Rouco
Sep 30, 2020·Mathematical Biosciences and Engineering : MBE·Yin Lin ChengYi Zhou
Jan 18, 2022·Microscopy Research and Technique·Amjad RehmanMazar Javed Awan

❮ Previous
Next ❯

Related Concepts

Related Feeds

Brain Imaging of Neural Circuits

Neural circuits are groups of interconnected neurons which carry out specific functions when activated. Imaging these neural circuits allows researches to further elucidate their mechanisms and functions. Follow this feed to stay up to date on brain imaging of neural circuits.

Brain Imaging of Neural Circuits (MDS)

Neural circuits are groups of interconnected neurons which carry out specific functions when activated. Imaging these neural circuits allows researches to further elucidate their mechanisms and functions. Follow this feed to stay up to date on brain imaging of neural circuits.

© 2022 Meta ULC. All rights reserved