TY - JOUR
T1 - DSRD-Net
T2 - Dual-stream residual dense network for semantic segmentation of instruments in robot-assisted surgery
AU - Mahmood, Tahir
AU - Cho, Se Woon
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/9/15
Y1 - 2022/9/15
N2 - 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.
AB - 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.
KW - DSRD-Net
KW - Gastrointestinal endoscopy and abdominal porcine procedures
KW - Minimally invasive surgery
KW - Surgical instruments segmentation
UR - http://www.scopus.com/inward/record.url?scp=85129468552&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.117420
DO - 10.1016/j.eswa.2022.117420
M3 - Article
AN - SCOPUS:85129468552
SN - 0957-4174
VL - 202
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 117420
ER -