Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is an effective technique for detecting colorectal polyps, which are closely associated with colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great significance, as it provides valuable information for diagnosis and surgery. Many networks have demonstrated better segmentation results. However, achieving accurate polyp segmentation remains a challenge due to the diverse in size, shape, texture and color of polyps. This paper proposes a Multi-Attention and Context Network (MACNet), which simulates the process of determining the segmentation region by clinical experts, incorporating the Balancing Attention Module (BAM), Non-local Information Statistical Attention module (Non-local), Position Rectify Module (PRM) and Focus Module (FM). BAM and PRM learn to adjust the distribution of attention in the feature map from six different perspectives: polyp region, surrounding mucosa, boundaries, channel axial, horizontal axial and vertical axial of the feature map. Non-local captures the connections between any two pixels in the feature map to supplement long-distance global dependence. FM uses context information of different scales to reason and refine the ambiguous regions in segmentation results and then achieves more accurate polyp segmentation. We evaluate the effectiveness of our network with six evaluation metrics on five polyp datasets, and it can be seen from results that our MACNet can achieve more accurate segmentation in general.
Supported by the National Natural Science Foundation of China under Grant No. 62072135 and No. 61672181.
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References
Vázquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017, 9 (2017). Article ID 4037190. https://doi.org/10.1155/2017/4037190
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. ar**v preprint ar**v:1706.05587 (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE (2018)
Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26
Ho, J., Kalchbrenner, N., Weissenborn, D., Salimans, T.: Axial attention in multidimensional transformers. ar**v preprint ar**v:1912.12180 (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Iwahori, Y., et al.: Automatic polyp detection in endoscope images using a hessian filter. In: MVA, pp. 21–24 (2013)
Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.D.: DoubleU-Net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 558–564. IEEE (2020)
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37
Jha, D., et al.: ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255. IEEE (2019)
Jia, X., **ng, X., Yuan, Y., **ng, L., Meng, M.Q.H.: Wireless capsule endoscopy: a new tool for cancer screening in the colon with deep-learning-based polyp recognition. Proc. IEEE 108(1), 178–197 (2019)
Lou, A., Guan, S., Loew, M.: CaraNet: context axial reverse attention network for segmentation of small medical objects. ar**v preprint ar**v:2108.07368 (2021)
Lou, A., Loew, M.: CFPNet: channel-wise feature pyramid for real-time semantic segmentation. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 1894–1898. IEEE (2021)
Mei, H., Ji, G.P., Wei, Z., Yang, X., Wei, X., Fan, D.P.: Camouflaged object segmentation with distraction mining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8772–8781 (2021)
Nguyen, T.-C., Nguyen, T.-P., Diep, G.-H., Tran-Dinh, A.-H., Nguyen, T.V., Tran, M.-T.: CCBANet: cascading context and balancing attention for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 633–643. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_60
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)
Srivastava, A., et al.: MSRF-Net: a multi-scale residual fusion network for biomedical image segmentation. ar**v preprint ar**v:2105.07451 (2021)
Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630–644 (2015)
Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1–9 (2018)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 699–708. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_66
Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 253–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_25
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS 2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
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The work was supported by the National Natural Science Foundation of China under Grant No. 62072135 and No. 61672181.
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Hao, X., Pan, H., Zhang, K., Chen, C., Bian, X., He, S. (2023). MACNet: Multi-Attention and Context Network for Polyp Segmentation. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_28
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