Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion

WEI Bo-wen; QUAN Hong-yan

doi:10.12263/DZXB.20220074

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Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion

PAPERS | 更新时间：2025-12-08

- Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2688-2697(2022)
- 作者机构：
  
  1.华东师范大学软件工程学院，上海 200062
  2.华东师范大学计算机学院，上海 200062
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220074
  CLC： TP391
- Received：13 January 2022，
  
  Revised：2022-03-15，
  
  Published：25 November 2022
- 稿件说明：
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魏博文,全红艳.基于语义与形态特征融合的语义分割网络[J].电子学报,2022,50(11):2688-2697.

WEI Bo-wen,QUAN Hong-yan.Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion[J].ACTA ELECTRONICA SINICA,2022,50(11):2688-2697.
魏博文,全红艳.基于语义与形态特征融合的语义分割网络[J].电子学报,2022,50(11):2688-2697. DOI： 10.12263/DZXB.20220074.

WEI Bo-wen,QUAN Hong-yan.Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion[J].ACTA ELECTRONICA SINICA,2022,50(11):2688-2697. DOI： 10.12263/DZXB.20220074.

摘要

视网膜血管检测有助于医生诊断视网膜疾病，而以往基于特征融合的算法难以解决视网膜血管检测中出现的漏分割问题，且分割准确率较低.本文对特征融合方式做出进一步探索，并提出一种基于语义与形态特征融合的算法，通过挖掘输入特征中蕴含的语义与形态信息，建模特征间的相关关系.随后，使用特征融合模块实现多模态特征自适应地融合.在公开数据集DRIVE以及STARE上的实验结果表明，文章算法优于现有的语义分割模型，尤其在敏感性上，比传统U-Net网络提升了8.20%.

Abstract

Retinal blood vessel detection is helpful for doctors to diagnose retinal diseases

but the previous algorithm based on feature fusion is difficult to solve the problem of missed segmentation in retinal blood vessel detection

and the segmentation accuracy is low. This paper further explores the feature fusion method and proposes an algorithm based on the fusion of semantic and morphological features. It models the correlation between features by mining the semantic and morphological information contained in the input features. Then

the feature fusion module realizes the adaptive fusion of multi-modal features. The experimental results on the public datasets DRIVE and STARE show that

the article algorithm is better than the existing semantic segmentation model

especially in sensitivity

which is 8.20% higher than the traditional U-Net.

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Keywords

references

ZANA F , KLEIN J C . Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation [J]. IEEE Transactions on Image Processing , 2001 , 10 ( 7 ): 1010 - 1019 .

SINGH N P , KUMAR R , SRIVASTAVA R . Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter [C]// International Conference on Computing , Communication & Automation . Noida : IEEE , 2015 : 1166 - 1170 .

RONNEBERGER O , FISCHER P , BROX T . U-net: convolutional networks for biomedical image segmentation [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich : Springer, Cham , 2015 : 234 - 241 .

ALOM M Z , YAKOPCIC C , TAHA T M , et al . Nuclei segmentation with recurrent residual convolutional neural networks based u-net(R2U-Net) [C]// NAECON 2018-IEEE National Aerospace and Electronics Conference . Dayton : IEEE , 2018 : 228 - 233 .

ZHOU Z , SIDDIQUEE M , 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 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE , 2016 : 770 - 778 .

HUANG G , LIU Z , MAATEN L VAN DER , et al . Densely connected convolutional networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Hawaii : IEEE , 2017 : 4700 - 4708 .

ZHANG S , FU H , YAN Y , et al . Attention guided network for retinal image segmentation [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention . Shenzhen : Springer, Cham , 2019 : 797 - 805 .

ZHANG J , ZHANG Y , XU X . Pyramid u-net for retinal vessel segmentation [C]// ICASSP2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP) . Toronto : IEEE , 2021 : 1125 - 1129 .

WOO S , PARK J , LEE J Y , et al . CBAM: convolutional block attention module [C]// Proceedings of the European Conference on Computer Vision(ECCV) . Munich : Springer , 2018 : 3 - 19 .

QIN Z , ZHANG P , WU F , et al . Fcanet: frequency channel attention networks [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Montreal : IEEE , 2021 : 783 - 792 .

ZHANG S , FU H , XU Y , et al . Retinal image segmentation with a structure-texture demixing network [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention . Lima : Springer, Cham , 2020 : 765 - 774 .

WU Y , YONG X , YANG S , et al . Multiscale network followed network model for retinal vessel segmentation [C]// International Conference on Medical Image Computing & Computer-assisted Intervention . Granada : Springer, Cham , 2018 : 119 - 126 .

WANG B , QIU S , HE H . Dual encoding u-net for retinal vessel segmentation [C]// International Conference on Medical Image Computing and Computer-Assisted Intervention . Shenzhen : Springer, Cham , 2019 : 84 - 92 .

GU Z , CHENG J , FU H , et al . Ce-net: context encoder network for 2d medical image segmentation [J]. IEEE Transactions on Medical Imaging , 2019 , 38 ( 10 ): 2281 - 2292 .

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 .

HOOVER A , KOUZNETSOVA V , 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 .

SALEHI S S M , ERDOGMUS D , GHOLIPOUR A . Tversky loss function for image segmentation using 3D fully convolutional deep networks [C]// Machine Learning in Medical Imaging: 8th International Workshop . Quebec : Springer , 2017 : 379 - 387 .

JIN Q , MENG Z , PHAM T D , et al . Dunet: a deformable network for retinal vessel segmentation [J]. Knowledge-Based Systems , 2019 , 178 : 149 - 162 .

MOU L , CHEN L , CHENG J , et al . Dense dilated network with probability regularized walk for vessel detection [J]. IEEE Transactions on Medical Imaging , 2019 , 39 ( 5 ): 1392 - 1403 .

YAN Z , XIN Y , CHENG K T . Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation [J]. IEEE Transactions on Biomedical Engineering , 2018 , 65 ( 9 ): 1912 - 1923 .

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 7132 - 7141 .

ISLAM M , VIBASHAN V S , JOSE V , et al . Brain tumor segmentation and survival prediction using 3D attention unet [C]// International MICCAI Brainlesion Workshop . Shenzhen : Springer, Cham , 2019 : 262 - 272 .

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