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Segmentation for tracking surgical instruments plays an important role inrobot-assisted surgery. Segmentation of surgical instruments contributes tocapturing accurate spatial information for tracking. In this paper, a novelnetwork, Refined Attention Segmentation Network, is proposed to simultaneouslysegment surgical instruments and identify their categories. The U-shape networkwhich is popular in segmentation is used. Different from previous work, anattention module is adopted to help the network focus on key regions, which canimprove the segmentation accuracy. To solve the class imbalance problem, theweighted sum of the cross entropy loss and the logarithm of the Jaccard indexis used as loss function. Furthermore, transfer learning is adopted in ournetwork. The encoder is pre-trained on ImageNet. The dataset from the MICCAIEndoVis Challenge 2017 is used to evaluate our network. Based on this dataset,our network achieves state-of-the-art performance 94.65% mean Dice and 90.33%mean IOU.

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network