33 citations · 75 across the 6 of their papers we have counts for
6 papers
2020 CATARACTS Semantic Segmentation Challenge
Imanol Luengo, Maria Grammatikopoulou, Rahim Mohammadi +37
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In…
Robust Medical Instrument Segmentation Challenge 2019
Tobias Ross, Annika Reinke, Peter M. Full +47
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…
BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation
Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang +5
Surgical instrument segmentation is extremely important for computer-assisted surgery. Different from common object segmentation, it is more challenging due to the large illuminati…
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
Zhen-Liang Ni, Gui-Bin Bian, Xiao-Hu Zhou +6
Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge d…
Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
Zhen-Liang Ni, Gui-Bin Bian, Zeng-Guang Hou +3
The real-time segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, it is still a challenging task to implement deep learning models to do r…
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
Zhen-Liang Ni, Gui-Bin Bian, Xiao-Liang Xie +3
Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial inf…