Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm

LIANG Liming; ZHAN Tao; LEI Kun; FENG Jun; TAN Lumin

doi:10.11999/JEIT220470

Volume 45 Issue 5

May 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1795-1806

LIANG Liming, ZHAN Tao, LEI Kun, FENG Jun, TAN Lumin. Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470

Citation:

LIANG Liming, ZHAN Tao, LEI Kun, FENG Jun, TAN Lumin. Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470

Citation:

PDF( 8167 KB)

Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm

doi: 10.11999/JEIT220470

School of Electrical Engineering Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (51365017, 61463018), The General Project of Jiangxi Provincial Natural Science Foundation (20192BAB205084), The Science and Technology Research Key Project, Department of Education, Jiangxi Province (GJJ170491)

Received Date: 2022-04-18
Rev Recd Date: 2022-07-13

Available Online: 2022-07-19

Publish Date: 2023-05-10

Abstract

Abstract

Considering the characteristics of irregular retinal blood vessel topology, complex morphology and diverse scale changes, a Multi-resolution Fusion Input U-Netword (MFIU-Net) is proposed to achieve accurate segmentation of retinal blood vessels. A rough segmentation network based on multi-resolution fusion input is designed to generate high-resolution features.. The improved ResNeSt is used to replace the traditional convolution to optimize the boundary features of blood vessel segmentation, and the parallel spatial activation module is embedded to capture more semantic and spatial information. Another U-shaped fine segmentation network is constructed to improve the microscopic representation and recognition ability of the model. Firstly, the multi-scale dense feature pyramid module to extract the multi-scale feature information of blood vessels is adopted at the bottom layer. Secondly, the feature adaptive module is used to enhance the feature fusion between coarse and fine networks to suppress irrelevant background noise. Thirdly, a detail-oriented double loss function fusion is designed to guide the network to focus on learning features. Experiments are carried out on the fundus data Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the REtinal (STARE) and Child Heart and Health Study (CHASE_DB1), the accuracy rates are 97.00%, 97.47% and 97.48%, the sensitivity is 82.73%, 82.86% and 83.24%, and the Area Under Cure (AUC) values are 98.74%, 98.90% and 98.93%, respectively. The overall performance of its model is better than that of existing algorithms.
- Retinal vessel segmentation,
- U-shaped network,
- Parallel space activation module,
- Multiscale dense feature pyramid module,
- Double loss function fusion

FullText(HTML)

References(31)

References

[1]	王娟, 赵建勇, 童龙. 老年2型糖尿病患者并发周围神经病变的影响因素分析[J]. 中国慢性病预防与控制, 2019, 27(1): 52–54. doi: 10.16386/j.cjpccd.issn.1004-6194.2019.01.014 WANG Juan, ZHAO Jianyong, and TONG Long. Analysis of the influencing factors of peripheral neuropathy in elderly patients with type 2 diabetes[J]. China Journal of Chronic Disease Prevention and Control, 2019, 27(1): 52–54. doi: 10.16386/j.cjpccd.issn.1004-6194.2019.01.014
[2]	YU Linfang, QIN Zhen, ZHUANG Tianming, et al. A framework for hierarchical division of retinal vascular networks[J]. Neurocomputing, 2020, 392: 221–232. doi: 10.1016/j.neucom.2018.11.113
[3]	KHAWAJA A, KHAN T M, KHAN M A U, et al. A multi-scale directional line detector for retinal vessel segmentation[J]. Sensors, 2019, 19(22): 4949. doi: 10.3390/s19224949
[4]	KHAWAJA A, KHAN T M, NAVEEDK, et al. An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser[J]. IEEE Access, 2019, 7: 164344–164361. doi: 10.1109/access.2019.2953259
[5]	田丰, 李莹, 王静. 基于多尺度小波变换融合的视网膜血管分割[J]. 光学学报, 2021, 41(4): 0410001. doi: 10.3788/AOS202141.0410001 TIAN Feng, LI Ying, and WANG Jing. Retinal blood vessel segmentation based on multi-scale wavelet transform fusion[J]. Acta Optica Sinica, 2021, 41(4): 0410001. doi: 10.3788/AOS202141.0410001
[6]	梁礼明, 盛校棋, 蓝智敏, 等. 基于多尺度滤波的视网膜血管分割算法[J]. 计算机应用与软件, 2019, 36(10): 190–196,204. doi: 10.3969/j.issn.1000-386x.2019.10.033 LIANG Liming, SHENG Xiaoqi, LAN Zhimin, et al. Retinal vessels segmentation algorithom based on multi-scale filtering[J]. Computer Applications and Software, 2019, 36(10): 190–196,204. doi: 10.3969/j.issn.1000-386x.2019.10.033
[7]	YANG Xin, LI Zhiqiang, GUO Yingqing, et al. DCU-Net: A deformable convolutional neural network based on cascade U-net for retinal vessel segmentation[J]. Multimedia Tools and Applications, 2022, 81(11): 15593–15607. doi: 10.1007/s11042-022-12418-w
[8]	CHEN Yixuan, DONG Yuhan, ZHANG Yi, et al. RNA-Net: Residual nonlocal attention network for retinal vessel segmentation[C]. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Canada, 2020: 1560–1565.
[9]	WANG Dongyi, HAYTHAM A, POTTENBURGH J, et al. Hard attention net for automatic retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3384–3396. doi: 10.1109/jbhi.2020.3002985
[10]	ZHANG Yuan, HE Miao, CHEN Zhineng, et al. Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation[J]. Expert Systems with Applications, 2022, 195: 116526. doi: 10.1016/j.eswa.2022.116526
[11]	IBTEHAZ N and RAHMAN M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020, 121: 74–87. doi: 10.1016/j.neunet.2019.08.025
[12]	ZHANG Hang, WU Chongruo, ZHANG Zhongyue, et al. ResNeSt: Split-attention networks[EB/OL]. https://arxiv.org/abs/2004.08955, 2022.
[13]	LI Di, DHARMAWAN D A, NG B P, et al. Residual U-Net for retinal vessel segmentation[C]. 2019 IEEE International Conference on Image Processing (ICIP), Taipei, China, 2019: 1425–1429.
[14]	ZHOU Tianyan, ZHAO Yong, and WU Jian. ResNeXt and Res2Net structures for speaker verification[C]. 2021 IEEE Spoken Language Technology Workshop (SLT), Shenzhen, China, 2021: 301–307.
[15]	HU Jie, SHEN Li, and SUN Gang. Squeeze-and-excitation networks[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7132–7141.
[16]	WANG Chang, ZHAO Zongya, REN Qiongqiong, et al. Dense U-net based on patch-based learning for retinal vessel segmentation[J]. Entropy, 2019, 21(2): 168. doi: 10.3390/e21020168
[17]	HU Peijun, LI Xiang, TIAN Yu, et al. Automatic pancreas segmentation in CT images with distance-based saliency-aware DenseASPP network[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(5): 1601–1611. doi: 10.1109/jbhi.2020.3023462
[18]	ABRAHAM N and KHAN N M. A novel focal tversky loss function with improved attention U-Net for lesion segmentation[C]. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019: 683–687.
[19]	ZHANG Guokai, SHEN Xiaoang, CHEN Sirui, et al. DSM: A deep supervised multi-scale network learning for skin cancer segmentation[J]. IEEE Access, 2019, 7: 140936–140945. doi: 10.1109/access.2019.2943628
[20]	STAAL J, ABRAMOFF M D, NIEMEIJER M, et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE Transactions on Medical Imaging, 2004, 23(4): 501–509. doi: 10.1109/tmi.2004.825627
[21]	HOOVER A D, KOUZNETSOVA V, and GOLDBAUM M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response[J]. IEEE Transactions on Medical Imaging, 2000, 19(3): 203–210. doi: 10.1109/42.845178
[22]	OWEN C G, RUDNICKA A R, MULLEN R, et al. Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina (CAIAR) program[J]. Investigative Ophthalmology & Visual Science, 2009, 50(5): 2004–2010. doi: 10.1167/iovs.08-3018
[23]	ALOM Z, HASAN M, YAKOPCIC C, et al. Recurrent residual convolutional neural network based on U-Net (R2u-Net) for medical image segmentation[EB/OL]. https://arxiv.org/abs/1802.06955, 2018.
[24]	ZHOU Zongwei, SIDDIQUEE M R, TAJBAKHSH N, et al. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 1856–1867. doi: 10.1109/tmi.2019.2959609
[25]	YU Changqian, XIAO Bin, GAO Changxin, et al. Lite-HRNet: A lightweight high-resolution network[C]. The 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 10435–10445.
[26]	ZHUANG Juntang. LadderNet: Multi-path networks based on U-Net for medical image segmentation[EB/OL]. https://arxiv.org/abs/1810.07810, 2018.
[27]	LI Di and RAHARDJA S. BSEResU-Net: An attention-based before-activation residual U-Net for retinal vessel segmentation[J]. Computer Methods and Programs in Biomedicine, 2021, 205: 106070. doi: 10.1016/j.cmpb.2021.106070
[28]	JIN Qiangguo, MENG Zhaopeng, PHAM T D, et al. DUNet: A deformable network for retinal vessel segmentation[J]. Knowledge-Based Systems, 2019, 178: 149–162. doi: 10.1016/j.knosys.2019.04.025
[29]	LI Xiang, JIANG Yuchen, LI Minglei, et al. Lightweight attention convolutional neural network for retinal vessel image segmentation[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1958–1967. doi: 10.1109/tii.2020.2993842
[30]	LV Yan, MA Hui, LI Jianian, et al. Attention guided U-Net with atrous convolution for accurate retinal vessels segmentation[J]. IEEE Access, 2020, 8: 32826–32839. doi: 10.1109/access.2020.2974027
[31]	YUAN Yuchen, ZHANG Lei, WANG Lituan, et al. Multi-level attention network for retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(1): 312–323. doi: 10.1109/jbhi.2021.3089201