RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments

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Laina, I., Rieke, N., Rupprecht, C., Vizcaíno, J.P., Eslami, A., Tombari, F., Navab, N.: Concurrent segmentation and localization for tracking of surgical instruments. In: International Conference on Medical Image Computing and Computer-assisted Intervention. pp. 664–672. Springer (2017)

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Attia, M., Hossny, M., Nahavandi, S., Asadi, H.: Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). pp. 3373–3378. IEEE (2017)

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Iglovikov, V., Shvets, A.: Ternausnet: U-Net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746 (2018)

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2018

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Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180 (2018)

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2018

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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. arXiv preprint arXiv:1905.08663 (2019)

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2019

Closest in time.