output
20162026
most citedSurgical Data Science: Enabling Next-Generation Surgery

578 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2018Show all

6 papers · 1 filter

cs.CV2018★ 12 cited

Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach

Thibaut Issenhuth, Vinkle Srivastav, Afshin Gangi +1

Purpose: Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can inde…

cs.CV2018★ 151 cited

Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos

Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux +1

Purpose: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgica…

cs.CV2018★ 38 cited

Future-State Predicting LSTM for Early Surgery Type Recognition

Siddharth Kannan, Gaurav Yengera, Didier Mutter +2

This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video. Early recognition algorithms can be beneficial to the developmen…

eess.SP2018

An Open Framework Enabling Electromagnetic Tracking in Image-Guided Interventions

Herman Alexander Jaeger, Stephen Hinds, Pádraig Cantillon-Murphy

Electromagnetic tracking (EMT) is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-l…

cs.CV2018★ 3 cited

Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos

Armine Vardazaryan, Didier Mutter, Jacques Marescaux +1

Surgical tool localization is an essential task for the automatic analysis of endoscopic videos. In the literature, existing methods for tool localization, tracking and segmentatio…

cs.CV2018★ 126 cited

RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

Andru Putra Twinanda, Gaurav Yengera, Didier Mutter +2

Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, howe…