• Optics and Precision Engineering
  • Vol. 31, Issue 18, 2765 (2023)
Ping XIA1,2, Guangyi ZHANG1,2, Bangjun LEI1,2,*, Yaobing ZOU1,2, and Tinglong TANG1,2
Author Affiliations
  • 1Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering, Three Gorges University, Yichang443002, China
  • 2College of Computer and Information Technology, Three Gorges University, Yichang44300, China
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    DOI: 10.37188/OPE.20233118.2765 Cite this Article
    Ping XIA, Guangyi ZHANG, Bangjun LEI, Yaobing ZOU, Tinglong TANG. Polyp image segmentation based on multi-scale ResNeSt-50 aggregation network and message passing[J]. Optics and Precision Engineering, 2023, 31(18): 2765 Copy Citation Text show less
    References

    [1] J SILVA, A HISTACE, O ROMAIN et al. Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery, 9, 283-293(2014).

    [2] 郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析[J]. 中华肿瘤杂志, 2019, 41(1): 19-28. doi: 10.3760/cma.j.issn.0253-3766.2019.01.005ZHENGR S, SUNK X, ZHANGS W, et al. Report of cancer epidemiology in China, 2015[J]. Chinese Journal of Oncology, 2019, 41(1): 19-28.(in Chinese). doi: 10.3760/cma.j.issn.0253-3766.2019.01.005

    [3] V ARAN, AP VICTORINO, LC THULER et al. Colorectal cancer: epidemiology, disease mechanisms and interventions to reduce onset and mortality. Clinical Colorectal Cancer, 15, 195-203(2016).

    [4] 江贵平, 秦文健, 周寿军, 等. 医学图像分割及其发展现状[J]. 计算机学报, 2015, 38(6)1222-1242. doi: 10.11897/SP.J.1016.2015.01222JIANGG P, QINW J, ZHOUS J, et al. State-of-the-art in medical image segmentation[J]. Chinese Journal of Computers, 2015, 38(6)1222-1242(in Chinese). doi: 10.11897/SP.J.1016.2015.01222

    [5] 田娟秀, 刘国才, 谷珊珊, 等. 医学图像分析深度学习方法研究与挑战[J]. 自动化学报, 2018, 44(3)401-424. doi: 10.16383/j.aas.2018.c170153TIANJ X, LIUG C, GUS S, et al. Deep learning in medical image analysis and its challenges[J]. Acta Automatica Sinica, 2018, 44(3)401-424(in Chinese). doi: 10.16383/j.aas.2018.c170153

    [6] 李少东,洪森,李世权等.人工智能利用图像深度学习诊疗消化道肿瘤的现状及展望[J].中国普外基础与临床杂志,2021,28(11):1524-1529. doi: 10.7507/1007-9424.202012127LISH D, HONGS, LISH Q, et al. Current situation and prospect of artificial intelligence in the diagnosis and treatment of. doi: 10.7507/1007-9424.202012127gastrointestinal tumors using image deep learning[J]. Chinese Journal of Bases and Clinics in General Surgery, 2021,28(11):1524-1529. doi: 10.7507/1007-9424.202012127

    [7] 吴锦珍, 谢玥, 王新颖. 人工智能在结直肠息肉性质鉴别中的应用进展[J]. 中华消化内镜杂志, 2020, 37(11):849-852. doi: 10.3760/cma.j.cn321463-20200610-00515WUJ Z, XIEY, WANGX Y. Application progress of artificial intelligence in identification of colorectal polyps[J]. Chinese Journal of Digestive Endoscopy, 2020, 37(11): 849-852. (in Chinese). doi: 10.3760/cma.j.cn321463-20200610-00515

    [8] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

    [9] O RONNEBERGER, P FISCHER, T BROX. U-Net Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, 234-241(2015).

    [10] Z ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet++: a Nested U-net Architecture for Medical Image Segmentation. arXiv, 1807-10165(2018). https://arxiv.org/abs/1807.10165

    [11] P H SMEDSRUD, M A RIEGLER et al. ResUNet: an advanced architecture for medical image segmentation, 225-2255(9).

    [12] D P FAN, G P JI, T ZHOU et al. PraNet Parallel Reverse Attention Network for Polyp Segmentation. Medical Image Computing and Computer Assisted Intervention-MICCAI 2020, 263-273(2020).

    [13] M A RIEGLER, D JOHANSEN et al. DoubleU-net: a deep convolutional neural network for medical image segmentation, 558-564(28).

    [14] Y Q FANG, D L ZHU, J H YAO et al. ABC-net: area-boundary constraint network with dynamical feature selection for colorectal polyp segmentation. IEEE Sensors Journal, 21, 11799-11809(2021).

    [15] R F ZHANG, G B LI, Z LI et al. Adaptive Context Selection for Polyp Segmentation. Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 253-262(2020).

    [16] 刘佳伟, 刘巧红, 李晓欧, 等. 一种改进的双U型网络的结肠息肉分割方法[J]. 光学学报, 2021, 41(18): 1810001. doi: 10.3788/aos202141.1810001LIUJ W, LIUQ H, LIX O, et al. Improved colonic polyp segmentation method based on double U-shaped network[J]. Acta Optica Sinica, 2021, 41(18): 1810001.(in Chinese). doi: 10.3788/aos202141.1810001

    [17] 撖子奇, 刘巧红, 凌晨, 等. 结合HarDNet和反向注意力的息肉分割方法[J]. 激光与光电子学进展, 2023, 60(2): 3788/LOP212665. doi: 10.3788/LOP212665HANZ Q, LIUQ H, LINGC, et al. Polyp segmentation method combining HarDNet and reverse attention[J]. Laser & Optoelectronics Progress, 2023, 60(2): 3788/LOP212665.(in Chinese). doi: 10.3788/LOP212665

    [18] H ZHANG, C R WU, Z Y ZHANG et al. ResNeSt: split-attention networks, 2735-2745(19).

    [19] S T LIU, D HUANG, Y H WANG. Receptive Field Block Net for Accurate and Fast Object Detection. Computer Vision-ECCV 2018, 404-419(2018).

    [20] Z ZHU, Z BIAN, J HOU et al. When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks. arXiv, 2004-08796(2020). https://arxiv.org/abs/2004.08796

    [21] M YAMADA, Y SAITO, H IMAOKA et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific Reports, 9, 14465(2019).

    [22] M J WAINWRIGHT, T S JAAKKOLA, A S WILLSKY. MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory, 51, 3697-3717(2005).

    [23] R SZELISKI, R ZABIH, D SCHARSTEIN et al. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1068-1080(2008).

    [24] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(27).

    [25] J HU, L SHEN, G SUN. Squeeze-and-excitation networks, 7132-7141(18).

    [26] X LI, W H WANG, X L HU et al. Selective kernel networks, 510-519(15).

    [27] S N XIE, R GIRSHICK, P DOLLÁR et al. Aggregated residual transformations for deep neural networks, 5987-5995(21).

    [28] H ZHANG, K K ZU, J LU et al. EPSANet an Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network. Computer Vision-ACCV 2022, 541-557(2023).

    [29] Z WU, L SU, Q M HUANG. Cascaded partial decoder for fast and accurate salient object detection, 3902-3911(15).

    [30] V KOLMOGOROV. Convergent tree-reweighted message passing for energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1568-1583(2006).

    [31] X YANG, X B GAO, D C TAO et al. An efficient MRF embedded level set method for image segmentation. IEEE Transactions on Image Processing, 24, 9-21(2015).

    [32] 夏平, 施宇, 雷帮军, 等. 复小波域混合概率图模型的超声医学图像分割[J]. 自动化学报, 2021, 47(1):185-196. doi: 10.16383/j.aas.c180132XIAP, SHIY, LEIB J, et al. Ultrasound medical image segmentation based on hybrid probabilistic graphical model in complex-wavelet domain[J]. Acta Automatica Sinica, 2021, 47(1):185-196.(in Chinese). doi: 10.16383/j.aas.c180132

    [33] J BERNAL, FJ SÁNCHEZ, G FERNÁNDEZ-ESPARRACH et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 43, 99-111(2015).

    [34] P H SMEDSRUD, M A RIEGLER et al. Kvasir-SEG a Segmented Polyp Dataset. MultiMedia Modeling, 451-462(2019).

    [35] N TAJBAKHSH, S R GURUDU, J M LIANG. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging, 35, 630-644(2016).

    [36] D VÁZQUEZ, J BERNAL et al. A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering, 2017, 4037190(2017).

    [37] L LIU, H JIANG, P HE et al. On the variance of the adaptive learning rate and beyond. arXiv e-prints(2019).

    [38] Y Q FANG, C CHEN, Y X YUAN et al. Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation. Lecture Notes in Computer Science, 302-310(2019).

    [39] X B QIN, Z C ZHANG, C Y HUANG et al. BASNet: boundary-aware salient object detection, 7471-7481(15).

    [40] J WEI, S H WANG, Q M HUANG. F³Net: fusion, feedback and focus for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12321-12328(2020).

    [41] D P FAN, M M CHENG, Y LIU et al. Structure-measure: a new way to evaluate foreground maps, 4558-4567(22).

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    Ping XIA, Guangyi ZHANG, Bangjun LEI, Yaobing ZOU, Tinglong TANG. Polyp image segmentation based on multi-scale ResNeSt-50 aggregation network and message passing[J]. Optics and Precision Engineering, 2023, 31(18): 2765
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