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M2ANet: Multi-branch and multi-scale attention network for medical image segmentation |
| Wei Xue(薛伟)1,†, Chuanghui Chen(陈创慧)1, Xuan Qi(戚轩)2,3, Jian Qin(秦健)1, Zhen Tang(唐振)1, and Yongsheng He(何永胜)2,3 |
1 School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China;
2 Department of Radiology, Maanshan People's Hospital, Maanshan 243099, China;
3 Maanshan Key Laboratory for Medical Image Modeling and Intelligent Analysis, Maanshan 243099, China |
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Abstract Convolutional neural networks (CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities. However, due to the inability to effectively capture global information from images, CNNs can easily lead to loss of contours and textures in segmentation results. Notice that the transformer model can effectively capture the properties of long-range dependencies in the image, and furthermore, combining the CNN and the transformer can effectively extract local details and global contextual features of the image. Motivated by this, we propose a multi-branch and multi-scale attention network (M2ANet) for medical image segmentation, whose architecture consists of three components. Specifically, in the first component, we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling. In the second component, we apply residual block to the well-known convolutional block attention module to enhance the network's ability to recognize important features of images and alleviate the phenomenon of gradient vanishing. In the third component, we design a multi-scale feature fusion module, in which we adopt adaptive average pooling and position encoding to enhance contextual features, and then multi-head attention is introduced to further enrich feature representation. Finally, we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets, particularly in the context of preserving contours and textures. The source code of M2ANet will be released at https://github.com/AHUT-MILAGroup/M2ANet.
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Received: 22 January 2025
Revised: 19 March 2025
Accepted manuscript online: 21 April 2025
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PACS:
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07.05.Pj
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(Image processing)
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42.30.Tz
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(Computer vision; robotic vision)
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87.57.nm
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(Segmentation)
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| Fund: Project supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China (Grant Nos. 2023AH040149 and 2024AH051915), the Anhui Provincial Natural Science Foundation (Grant No. 2208085MF168), the Science and Technology Innovation Tackle Plan Project of Maanshan (Grant No. 2024RGZN001), and the Scientific Research Fund Project of Anhui Medical University (Grant No. 2023xkj122). |
Corresponding Authors:
Wei Xue
E-mail: xuewei@ahut.edu.cn
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Cite this article:
Wei Xue(薛伟), Chuanghui Chen(陈创慧), Xuan Qi(戚轩), Jian Qin(秦健), Zhen Tang(唐振), and Yongsheng He(何永胜) M2ANet: Multi-branch and multi-scale attention network for medical image segmentation 2025 Chin. Phys. B 34 080703
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[1] Luo L, Wang X, Lin Y, Ma X, Tan A, Chan R, Vardhanabhuti V, Chu W C, Cheng K T and Chen H 2024 IEEE Rev. Biomed. Eng. 18 130
[2] Zhao Y, Gui W, Chen Z, Tang J and Li L 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, January 17–18, 2005, Shanghai, China, p. 6492
[3] Jiang F, Frater M R and Pickering M 2012 International Conference on Digital Image Computing Techniques and Applications, December 03–05, 2012, Fremantle, WA, Australia, p. 1
[4] Gould S, Gao T and Koller D 2009 Neural Information Processing Systems, December 7–11, 2009, USA, p. 366
[5] Wang R, Lei T, Cui R, Zhang B, Meng H and Nandi A K 2022 IET Image Process. 16 1243
[6] Ruan J, Li J and Xiang S 2024 arXiv: 2402.02491
[7] Lou A, Guan S and Loew M 2021 Proc. SPIE 11596, Med. Imaging 2021: Image Process., February 15–20, 2021, California, USA, p. 115962T
[8] Brahim A S, Abdelhamid E H and Aicha M 2018 Procedia Comput. Sci. 127 109
[9] Deb S D and Jha R K 2023 IEEE Trans. Radiat. Plasma. Med. Sci. 7 151
[10] Ma J, Hou K, Bao S and Chen C 2011 Chin. Phys. B 20 028701
[11] Ronneberger O, Fischer P and Brox T 2015 18th International Conference Medical Image Computing and Computer-Assisted Intervention, October 5–9, 2015, Munich, Germany, p. 234
[12] Zhou Z, SiddiqueeMMR, Tajbakhsh N and Liang J 2018 Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, September 20, 2018, Granada, Spain, p. 3
[13] Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y and Wu J 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 04–08, 2020, Barcelona, Spain, p. 1055
[14] Ibtehaz N and Rahman M S 2020 Neural Networks 121 74
[15] Sinha A and Dolz J 2021 IEEE J. Biomed. Health Inform. 25 121
[16] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J and Houlsby N 2020 arXiv: 2010.11929
[17] Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S and Guo B 2021 Proceedings of the IEEE/CVF International Conference on Computer Vision, October 10–17, 2021, Montreal, QC, Canada, p. 9992
[18] Wang H, Cao P,Wang J and Zaiane O R 2022 Proceedings of the AAAI Conference on Artificial Intelligence, February 22–March 1, 2022, held virtually, p. 2441
[19] Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q and Wang M 2022 European Conference on Computer Vision, October 23–27, 2022, Tel Aviv, Israel, p. 205
[20] Ibtehaz N and Kihara D 2023 26th International Conference Medical Image Computing and Computer Assisted Intervention, October 8–12, 2023, Vancouver, BC, Canada, p. 692
[21] He J, Deng Z and Qiao Y 2019 IEEE/CVF International Conference on Computer Vision, October 27–November 02, 2019, Seoul, Korea (South), p. 3561
[22] Poudel S and Lee S W 2021 Appl. Soft Comput. 109 107445
[23] Valanarasu J M J and Patel V M 2022 25th International Conference Medical Image Computing and Computer Assisted Intervention, September 18–22, 2022, Singapore, p. 23
[24] Wang Z, Min X, Shi F, Jin R, Nawrin S S, Yu I and Nagatomi R 2022 25th International Conference, Singapore Medical Image Computing and Computer Assisted Intervention, September 18–22, 2022, Singapore, p. 517
[25] Cheng T, Zhao R, Wang S, Wang R and Ma H 2024 Chin. Phys. B 33 040303
[26] Wang S,Wang K, Cheng T, Zhao R, Ma H and Guo S 2024 Chin. Phys. B 33 060310
[27] Pan H, Liu M, Ge H and Yuan Q 2024 Chin. Phys. B 31 120701
[28] Lin T, Dollar P, Girshick R, He K, Hariharan B and Belongie S 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, HI, USA, p. 936
[29] Liu S, Huang D and Wang Y 2018 Proceedings of the European Conference on Computer Vision, September 8–14, 2018, Munich, Germany, p. 404
[30] Xu Q, Ma Z, Na H and Duan W 2023 Comput. Biol. Med. 154 106626
[31] Rahman M M and Marculescu R 2023 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, January 2–7, 2023, Waikoloa, HI, USA, p. 6211
[32] Rahman M M, Munir M and Marculescu R 2024 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 16–22, 2024, Seattle, WA, USA, p. 11769
[33] Zhao X, Jia H, Pang Y, Lv L, Tian F, Zhang L, Sun W and Lu H 2023 arXiv: 2303.10894
[34] Brauwers G and Frasincar F 2023 IEEE Trans. Knowl. Data. Eng. 35 3279
[35] Oktay O, Schlemper J, Folgoc L L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N Y, Kainz B, Glocker B and Rueckert D 2018 arXiv: 1804.03999
[36] Michael Y, Evis S, Carola S B and Leonardo R 2021 Comput. Biol. Med. 137 104815
[37] Ruan J, Xie M, Gao J, Liu T and Fu Y 2023 Medical Image Computing and Computer Assisted Intervention, October 8–12, 2023, Vancouver, BC, Canada, p. 481
[38] Nam J, Syazwany N S, Kim S J and Lee S C 2024 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 16–22, 2024, Seattle, WA, USA, p. 1148
[39] Woo S, Park J, Lee J Y and Kweon I S 2018 Proceedings of the European Conference on Computer Vision, September 8–14, 2018, Munich, Germany, p. 3
[40] Yu Q, Zhao X, Pang Y, Zhang L and Lu H 2024 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 16–22, 2024, Seattle, WA, USA, p. 3921
[41] Al-Dhabyani W, Gomaa M, Khaled H and Fahmy A 2020 Data Brief 28 104863
[42] Noel C F C, David G, M Emre C, Brian H, Michael A M, Stephen W D, Aadi K, Konstantinos L, Nabin M, Harald K and Allan H 2018 IEEE 15th International Symposium on Biomedical Imaging, April 4– 7, 2018, Washington, DC, USA, p. 168
[43] Sirinukunwattana K, Pluim J PW, Chen H, Qi X, Heng P, Guo Y,Wang L, Matuszewski B J, Bruni E, Sanchez U, Bohm A, Ronneberger O, Cheikh B B, Racoceanu D, Kainz P, Pfeiffer M, Urschler M, Snead D R J and Rajpoot N M 2017 Med. Image Anal. 35 489
[44] Jha D, Hicks S A, Emanuelsen K, Johansen H, Johansen D, Lange T D, Riegler M A and Halvorsen P 2020 arXiv: 2012.15244
[45] Dice L R 1945 Ecology 26 297
[46] Jaccard P 1912 New Phytol. 11 37 |
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