Abstract
Semantic parsing of surgical instruments provides essential information for further control. Due to the visual variations in surgery scenes, it challenging for automate segmentation task of instruments. In this paper, we proposed PBANet, which is short for Part-Boundary-Aware Networks, decomposing the instrument segmentation into two sub-tasks. An encoder-decoder architecture is adopted to predict the part-aware distance map that highlights the spatial structure of instruments. The segmentation mask is then obtained via the sigmoid function. We further propose to use a multi-scale dilation loss to reduce the boundary confusion. Empirical evaluations are performed on EndoVis2017 sub-challenge, demonstrating that the proposed method achieves superior performance compared to baseline methods.
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Notes
- 1.
The main contribution of this paper is not the network architecture. Therefore, it can be adapted to other encoder-decoder networks.
- 2.
The width of contour is set to 1 in this section.
- 3.
References
Allan, M., et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)
Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Dangi, S., Linte, C.A., Yaniv, Z.: A distance map regularized CNN for cardiac cine MR image segmentation. Med. Phys. 46(12), 5637–5651 (2019)
Gao, N., et al.: SSAP: single-shot instance segmentation with affinity pyramid. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 642–651 (2019)
GarcÃa-Peraza-Herrera, L.C., et al.: ToolNet: holistically-nested real-time segmentation of robotic surgical tools. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5717–5722. IEEE (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ni, Z.L., Bian, G.B., Xie, X.L., Hou, Z.G., Zhou, X.H., Zhou, Y.J.: RASNet: segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5735–5738. IEEE (2019)
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)
Qin, F., Lin, S., Li, Y., Bly, R.A., Moe, K.S., Hannaford, B.: Towards better surgical instrument segmentation in endoscopic vision: multi-angle feature aggregation and contour supervision. arXiv preprint arXiv:2002.10675 (2020)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Shvets, A.A., Rakhlin, A., Kalinin, A.A., Iglovikov, V.I.: Automatic instrument segmentation in robot-assisted surgery using deep learning. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 624–628. IEEE (2018)
Zhang, H., et al.: Context encoding for semantic segmentation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 7151–7160 (2018)
Acknowledgement
This work was partly supported by the National Key R&D Program of China (2019YFB1311503), NSFC (61375048,61661010,61977046,62003208), Committee of Science and Technology, Shanghai, China (19510711200) and Shanghai Sailing Program (20YF1420800).
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Liu, J., Qiao, Y., Yang, J., Gu, Y. (2020). Part-Boundary-Aware Networks for Surgical Instrument Parsing. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_82
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