Demo Data from VLDB 2026 / reviewers in the wild / expert
Nicolas Golse
dblp:226/6348
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-5261-1538ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
3D vision · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › geometric estimation › registration
non-rigid registration |
0.3 | 1 | 2018 | Marker-Based Registration for Large Deformations - Application to Open Liver Surgery · ICRA 2018 |
Medical and health informatics
surgical navigation |
0.3 | 1 | 2018 | Marker-Based Registration for Large Deformations - Application to Open Liver Surgery · ICRA 2018 |
Methods — techniques the papers use, named apart from their topics
infrared marker tracking · 0.7nonrigid registration · 0.3non-rigid registration · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Physics-Based Deep Neural Network for Augmented Reality During Liver Surgery
Jean-Nicolas Brunet, Andrea Mendizabal, Antoine Petit 0003, Nicolas Golse, Eric Vibert, Stephane Cotin |
MICCAI (5) | 4 |
| 2018 | Marker-Based Registration for Large Deformations - Application to Open Liver SurgeryabstractThis paper introduces an Augmented Reality (AR) system for open liver surgery. Although open surgery remains the gold-standard for the treatment of complex tumors and central lesions, technological issues actually prevent using AR with sufficient accuracy for clinical use. We propose a markers-based method allowing for the tracking and the deformation of a preoperative model in real-time during the surgery. Markers are manually placed on the surface of the organ after opening the abdominal cavity, and tracked in real-time by a set of infrared cameras. Our framework is composed of both a nonrigid initial registration method, providing an estimation of the location of the markers in the preoperative model, and a realtime tracking algorithm to deform the model during the surgery (even for large deformation or partial occlusion of the organ). The method is validated on both synthetic and ex-vivo samples; in addition, we demonstrate its applicability in the operating room during a liver resection surgery on a human patient. Preliminary studies provided promising results to improve the location of tumors, and to help surgeons into planning the ideal resection intraoperatively. Yinoussa Adagolodjo, Nicolas Golse, Eric Vibert, Michel de Mathelin, Stephane Cotin, Hadrien Courtecuisse |
ICRA | 2 |