Abstract
Colorectal cancer (CRC) caused by polyps has a high mortality rate worldwide. Accurately segmenting the polyp from colonoscopy images is important for the clinical treatment of CRC. The traditional method of polyp segmentation involves the physician manually marking the location of the polyp, resulting in unreliable segmentation results. The complex structure of polyps, the low contrast with mucosal tissue, and the fact that polyp boundary is usually hidden in the background make the task of polyp segmentation extremely challenging. To address these issues, we propose a boundary-aware polyp segmentation network. Specifically, we first propose an attention-aware location module to accurately identify the primary location of polyp. In order to improve the missing polyp portion in the initial region prediction and to mine the polyp boundary hidden in the background, we propose a residual pyramid convolution. Further, we propose a boundary-guided refinement module for more accurate segmentation in order to use the boundary information provided from residual pyramid convolution for constrained polyp region prediction. Extensive experiments show that our proposed network has advantages over existing state-of-the-art methods on five challenging polyp datasets.
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References
Akbari, M., et al.: Polyp segmentation in colonoscopy images using fully convolutional network. In: IEEE EMBC, pp. 69–72. IEEE (2018)
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. CMIG 43, 99–111 (2015)
Chen, S., Tan, X., Wang, B., Hu, X.: Reverse attention for salient object detection. In: ECCV, pp. 234–250 (2018)
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: IEEE ICCV, pp. 4548–4557 (2017)
Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (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
Fang, Y., Chen, C., Yuan, Y., Tong, K.: Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 302–310. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_34
Fu, J., et al.: Dual attention network for scene segmentation. In: IEEE CVPR, pp. 3146–3154 (2019)
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019)
Hou, Q., Zhang, L., Cheng, M.M., Feng, J.: Strip pooling: rethinking spatial pooling for scene parsing. In: IEEE CVPR, pp. 4003–4012 (2020)
Huang, C.H., Wu, H.Y., Lin, Y.L.: HarDNet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS. arXiv preprint arXiv:2101.07172 (2021)
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: CCNet: Criss-cross attention for semantic segmentation. In: IEEE ICCV, pp. 603–612 (2019)
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: IEEE ISM, pp. 225–2255. IEEE (2019)
Jia, X., Xing, X., Yuan, Y., Xing, 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)
Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: IEEE CVPR, pp. 3194–3203 (2016)
Lin, Z., et al.: A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017)
Mamonov, A.V., Figueiredo, I.N., Figueiredo, P.N., Tsai, Y.H.R.: Automated polyp detection in colon capsule endoscopy. IEEE TMI 33(7), 1488–1502 (2014)
Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: CVPR, pp. 248–255 (2014)
Máttyus, G., Luo, W., Urtasun, R.: Deeproadmapper: extracting road topology from aerial images. In: IEEE ICCV, pp. 3438–3446 (2017)
Murugesan, B., Sarveswaran, K., Shankaranarayana, S.M., Ram, K., Joseph, J., Sivaprakasam, M.: Psi-net: shape and boundary aware joint multi-task deep network for medical image segmentation. In: IEEE EMBC, pp. 7223–7226. IEEE (2019)
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Basnet: boundary-aware salient object detection. In: IEEE CVPR, pp. 7479–7489 (2019)
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
Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: DiSAN: directional self-attention network for RNN/CNN-free language understanding. In: AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. IJCARS 9(2), 283–293 (2014)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE TMI 35(2), 630–644 (2015)
Vázquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017 (2017)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: IEEE CVPR, pp. 7794–7803 (2018)
Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. In: IEEE ICCV, pp. 7264–7273 (2019)
Yin, Z., Liang, K., Ma, Z., Guo, J.: Duplex contextual relation network for polyp segmentation. arXiv preprint arXiv:2103.06725 (2021)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: ICML, pp. 7354–7363. PMLR (2019)
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
Acknowledgement
This work is partially supported by the Natural Science Foundation of China (No. 61802336), and Yangzhou University“Qinglan Project”.
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Lu, L. et al. (2022). Boundary-Aware Polyp Segmentation Network. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_6
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DOI: https://doi.org/10.1007/978-3-031-18916-6_6
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