Demo Data from EDBT 2026 / reviewers in the wild / expert
Paul Baksic
dblp:274/9166
· DBLP profile ↗
2ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 1 since 2021Systems, architecture and hardware · 2 · 2 first-author · 1 since 2021
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
2 papers |
Robot manipulation · 83% Motion planning and robot control · 17% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Medical and health informatics · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › medical robotics
needle insertion |
0.9 | 2 | 2021 | Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021 Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulation · ICRA 2020 |
Robotics › Robot manipulation › medical robotics
needle steering |
0.9 | 2 | 2021 | Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021 Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulation · ICRA 2020 |
Robotics › Robot manipulation › medical robotics
physiological motion compensation |
0.5 | 1 | 2021 | Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021 |
Robotics › Motion planning and robot control › teleoperation
shared control |
0.5 | 1 | 2021 | Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021 |
Methods — techniques the papers use, named apart from their topics
inverse finite element simulation · 1.0closed-loop control · 1.0inverse problem solving · 0.9finite element simulation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulationabstractThis paper deals with the problem of needle steering in deformable tissues subject to physiological motions. A novel shared control method is proposed, which combines an automatic needle steering algorithm with the motions applied by the radiologist, in order to place the needle tip at the desired location. The core motivation is to leave potentially dangerous decisions and actions to the practitioner, whereas complex non-intuitive manipulations of the needle are performed automatically, in particular to compensate for breathing motions. The most original part of the present work lies in the method used to combine user inputs with a closed-loop automatic needle steering control method based on inverse Finite Element simulations. The method is evaluated with a realistic virtual environment using 2D X-ray projection images. The results are compared with those obtained with a fully teleoperated system, on the one hand, and with a fully automatic solution, on the other hand. These experiments show that the shared control solution allows for a better needle tip placement when only projection imaging is available. Paul Baksic, Hadrien Courtecuisse, Bernard Bayle |
ICRA | 1 |
| 2020 | Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulationabstractThis paper introduces a method for robotic steering of a flexible needle inside moving and deformable tissues. The method relies on a set of objective functions allowing to automatically steer the needle along a predefined path. In order to follow the desired trajectory, an inverse problem linking the motion of the robot end effector with the objective functions is solved using a Finite Element simulation. The main contribution of the article is the new constraint-based formulation of the objective functions allowing to: 1) significantly reduce the computation time; 2) increase the accuracy and stability of the simulation-guided needle insertion. The method is illustrated, and its performances are characterized in a realistic framework, using a direct simulation of the respiratory motion generated from in vivo data of a pig. Despite the highly non-linear behavior of the numerical simulation and the significant deformations occurring during the insertion, the obtained performances enable the possibility to follow the trajectory with the desired accuracy for medical purpose. Paul Baksic, Hadrien Courtecuisse, Christian Duriez, Bernard Bayle |
ICRA | 1 |