A high resolution representation network with multi-path scale for retinal vessel segmentation

被引:32
作者
Lin, Zefang [1 ]
Huang, Jianping [1 ]
Chen, Yingyin [1 ]
Zhang, Xiao [1 ]
Zhao, Wei [1 ]
Li, Yong [1 ]
Lu, Ligong [1 ]
Zhan, Meixiao [1 ]
Jiang, Xiaofei [1 ,2 ]
Liang, Xiong [3 ]
机构
[1] Jinan Univ, Zhuhai Hosp, Zhuhai Peoples Hosp, Zhuhai Intervent Med Ctr,Zhuhai Precis Med Ctr, Zhuhai 519000, Guangdong, Peoples R China
[2] Jinan Univ, Zhuhai Hosp, Zhuhai Peoples Hosp, Dept Cardiol, Zhuhai 519000, Guangdong, Peoples R China
[3] Jinan Univ, Zhuhai Hosp, Zhuhai Peoples Hosp, Dept Obstet, Zhuhai 519000, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Retinal vessels segmentation; Deep learning; High resolution; Multi-path scale; CONDITIONAL RANDOM-FIELD; BLOOD-VESSELS; IMAGES; MODEL;
D O I
10.1016/j.cmpb.2021.106206
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Background and objectives: Automatic retinal vessel segmentation (RVS) in fundus images is expected to be a vital step in the early image diagnosis of ophthalmologic diseases. However, it is a challenging task to detect the retinal vessel accurately mainly due to the vascular intricacies, lesion areas and optic disc edges in retinal fundus images. Methods: In this paper, we propose a high resolution representation network with multi-path scale (MPS-Net) for RVS aiming to improve the performance of extracting the retinal blood vessels. In the MPS-Net, there exist one high resolution main road and two lower resolution branch roads where the proposed multi-path scale modules are embedded to enhance the representation ability of network. Besides, in order to guide the network focus on learning the features of hard examples in retinal images, we design a hard-focused cross-entropy loss function. Results: We evaluate our network structure on DRIVE, STARE, CHASE and synthetic images and the quantitative comparisons with respect to the existing methods are presented. The experimental results show that our approach is superior to most methods in terms of F1-score, sensitivity, G-mean and Matthews correlation coefficient. Conclusions: The promising segmentation performances reveal that our method has potential in real-world applications and can be exploited for other medical images with further analysis. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:15
相关论文
共 41 条
[1]   Trainable COSFIRE filters for vessel delineation with application to retinal images [J].
Azzopardi, George ;
Strisciuglio, Nicola ;
Vento, Mario ;
Petkov, Nicolai .
MEDICAL IMAGE ANALYSIS, 2015, 19 (01) :46-57
[2]   A tutorial on the cross-entropy method [J].
De Boer, PT ;
Kroese, DP ;
Mannor, S ;
Rubinstein, RY .
ANNALS OF OPERATIONS RESEARCH, 2005, 134 (01) :19-67
[3]   Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation [J].
Fathi, Abdolhossein ;
Naghsh-Nilchi, Ahmad Reza .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2013, 8 (01) :71-80
[4]   An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation [J].
Fraz, Muhammad Moazam ;
Remagnino, Paolo ;
Hoppe, Andreas ;
Uyyanonvara, Bunyarit ;
Rudnicka, Alicja R. ;
Owen, Christopher G. ;
Barman, Sarah A. .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012, 59 (09) :2538-2548
[5]   BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation [J].
Guo, Song ;
Wang, Kai ;
Kang, Hong ;
Zhang, Yujun ;
Gao, Yingqi ;
Li, Tao .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2019, 126 :105-113
[6]   Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods [J].
Hashemzadeh, Mahdi ;
Azar, Baharak Adlpour .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2019, 95 :1-15
[7]   Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :1026-1034
[8]   Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response [J].
Hoover, A ;
Kouznetsova, V ;
Goldbaum, M .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2000, 19 (03) :203-210
[9]   Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function [J].
Hu, Kai ;
Zhang, Zhenzhen ;
Niu, Xiaorui ;
Zhang, Yuan ;
Cao, Chunhong ;
Xiao, Fen ;
Gao, Xieping .
NEUROCOMPUTING, 2018, 309 :179-191
[10]  
Huazhu Fu, 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9901, P132, DOI 10.1007/978-3-319-46723-8_16