DSRD-Net: Dual-stream residual dense network for semantic segmentation of instruments in robot-assisted surgery

Abstract

In conventional robot-assisted minimally invasive procedures (RMIS), surgeons have narrow visual and complex working spaces, along with specular reflection, blood, camera-lens fogging, and complex backgrounds, which increase the risk of human error and tissue damage. The use of deep learning-based techniques can decrease these risks by providing segmented instruments, real-time tracking, pose estimation, and surgeons' skill assessment. Recently, several deep learning-based methods have been proposed for surgical instrument segmentation. These methods have shown significant performance for the RMIS. However, we found that most of these methods still have scope for improvement in terms of accuracy, robustness, and computational cost. In addition, gastrointestinal pathologies have not been explored in previous studies. Therefore, we propose a dual-stream residual dense network (DSRD-Net), an accurate and robust deep learning-based surgical instrument segmentation method that mainly utilizes the strength of residual, dense, and atrous spatial pyramid pooling architectures. Our proposed method was tested on publicly available gastrointestinal endoscopy (the Kvasir-Instrument Dataset) and abdominal porcine procedures datasets (The 2017 Robotic Instrument Segmentation Challenge Dataset). The experimental results show that the proposed method outperforms the state-of-the-art methods.