1 citations · 1 across the 3 of their papers we have counts for
7 papers
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +97
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…
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,…
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
Tobias Rueckert, David Rauber, Raphaela Maerkl +58
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…
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…
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…