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Nicolas Golse

dblp:226/6348 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › geometric estimation › registration
non-rigid registration
0.312018
Marker-Based Registration for Large Deformations - Application to Open Liver Surgery · ICRA 2018
Medical and health informatics
surgical navigation
0.312018
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
YearPublicationVenuePosition
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 Surgery
abstract
This 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
ICRA2