Abstract
Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be flexibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71\(\%\) mean Dice and 95.62\(\%\) mean IOU.
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Acknowledgment
This research is supported by the National Key Research and Development Program of China (Grant 2017YFB1302704), the National Natural Science Foundation of China (Grants 61533016, U1713220), the Beijing Science and Technology Plan (Grant Z191100002019013) and the Strategic Priority Research Program of CAS (Grant XDBS01040100).
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Ni, ZL. et al. (2019). RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_13
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DOI: https://doi.org/10.1007/978-3-030-36711-4_13
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