Hybrid U-Net: Instrument Semantic Segmentation in RMIS

Yue Wang, Huajian Song, Guangyuan Pan, Qingguo Xiao, Zhiyuan Bai, Ancai Zhang, Jianlong Qiu

Abstract: Accurate semantic segmentation of surgical instruments from images captured by the laparoscopic system plays a crucial role in ensuring the reliability of vision-based Robot-Assisted Minimally Invasive Surgery. Despite numerous notable advancements in semantic segmentation, the achieved segmentation accuracy still falls short of meeting the requirements for surgical safety. To enhance the accuracy further, we propose several modifications to a conventional medical image segmentation network, including a modified Feature Pyramid Module. Within this modified module, Patch-Embedding with varying rates and Self-Attention Blocks are employed to mitigate the loss of feature information while simultaneously expanding the receptive field. As for the network architecture, all feature maps extracted by the encoder are seamlessly integrated into the proposed modified Feature Pyramid Module via element-wise connections. The resulting output from this module is then transmitted to the decoder blocks at each stage. Considering these hybrid properties, the proposed method is called Hybrid U-Net. Subsequently, multiple experiments were conducted on two available medical datasets and the experimental results reveal that our proposed method outperforms the recent methods in terms of accuracy on both medical datasets.