10 papers · 1 filter
Training-free Temporal Object Tracking in Surgical Videos
Subhadeep Koley, Abdolrahim Kadkhodamohammadi, Santiago Barbarisi +2
Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical…
Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery
Muhammad Asad, Emanuele Colleoni, Pritesh Mehta +7
Purpose: Monocular depth estimation (MDE) is vital for scene understanding in minimally invasive surgery (MIS). However, endoscopic video sequences are often contaminated by smoke,…
Graph Neural Networks for Surgical Scene Segmentation
Yihan Li, Nikhil Churamani, Maria Robu +2
Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle w…
Learning from Single Timestamps: Complexity Estimation in Laparoscopic Cholecystectomy
Dimitrios Anastasiou, Santiago Barbarisi, Lucy Culshaw +4
Purpose: Accurate assessment of surgical complexity is essential in Laparoscopic Cholecystectomy (LC), where severe inflammation is associated with longer operative times and incre…
Zero-shot Monocular Metric Depth for Endoscopic Images
Nicolas Toussaint, Emanuele Colleoni, Ricardo Sanchez-Matilla +5
Monocular relative and metric depth estimation has seen a tremendous boost in the last few years due to the sharp advancements in foundation models and in particular transformer ba…
Temporal Cluster Assignment for Efficient Real-Time Video Segmentation
Ka-Wai Yung, Felix J. S. Bragman, Jialang Xu +3
Vision Transformers have substantially advanced the capabilities of segmentation models across both image and video domains. Among them, the Swin Transformer stands out for its abi…