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S. O. H. de Looijer

dblp:274/9480 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › medical imaging › ultrasound imaging
breast ultrasound imaging
0.412020
Automated robotic breast ultrasound acquisition using ultrasound feedback · ICRA 2020
Medical and health informatics › medical robotics
robotic ultrasound
0.412020
Automated robotic breast ultrasound acquisition using ultrasound feedback · ICRA 2020
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.112020
Automated robotic breast ultrasound acquisition using ultrasound feedback · ICRA 2020

Methods — techniques the papers use, named apart from their topics

visual servoing · 0.9confidence map · 0.9
YearPublicationVenuePosition
2020 Automated robotic breast ultrasound acquisition using ultrasound feedback
abstract
Current challenges in automated robotic breast ultrasound (US) acquisitions include keeping acoustic coupling between the breast and the US probe, minimizing tissue deformations and safety. In this paper, we present how an autonomous 3D breast US acquisition can be performed utilizing a 7DOF robot equipped with a linear US transducer. Robotic 3D breast US acquisitions would increase the diagnostic value of the modality since they allow patient specific scans and have a high reproducibility, accuracy and efficiency. Additionally, 3D US acquisitions allow more flexibility in examining the breast and simplify registration with preoperative images like MRI. To overcome the current challenges, the robot follows a reference- based trajectory adjusted by a visual servoing algorithm. The reference trajectory is a patient specific trajectory coming from e.g. an MRI. The visual servoing algorithm commands in-plane rotations and corrects the probe contact based on confidence maps. A safety aware, intrinsically passive framework is utilised to actuate the robot. The approach is illustrated with experiments on a phantom, which show that the robot only needs minor pre-procedural information to consistently image the phantom while relying mainly on US feedback.
Marcel K. Welleweerd, Antonius Gerardus de Groot, S. O. H. de Looijer, Françoise J. Siepel, Stefano Stramigioli
ICRA3