Abstract
With the rapid development of robot assisted surgery, the segmentation of surgical instruments becomes more and more important. However, compared with the natural scene segmentation, surgical instrument segmentation is more difficult. To solve this problem, we improve a high and low resolution fusion module, which aims to extract detail information and context information from the fusion feature map of high and low resolution. Then, in the last layer of the encoder, we propose the Enhanced Multi-scale Receptive Field module to generate more available receptive fields. Our method is validated on 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset, and the result is better than the other methods. The extended experiment is carried out on the dataset of our surgical soft robot which has a content implementation.
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