Simulation-based dataset acquisition for robotic cardiac ultrasound examinations

Kang, Shuping, Daniels, Thomas, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 and Hicks, Yulia ORCID: https://orcid.org/0000-0002-7179-4587 2024. Simulation-based dataset acquisition for robotic cardiac ultrasound examinations. Presented at: 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024), Seville, Spain, 11-13 September 2024. Procedia Computer Science. , vol.246 Elsevier, pp. 3967-3976. 10.1016/j.procs.2024.09.171

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Abstract

Over the past decade, automating ultrasound scanning has been the subject of intense research. However, training a robot to perform automated ultrasound examinations requires a substantial corpus of training data. Traditionally, researchers have sought to obtain such data either through publicly available datasets or by engaging professional sonographers in experiments aimed at dataset generation. The former approach often yields incomplete datasets insufficient for specialized research objectives, while the latter entails logistical challenges, including the necessity for frequent manual experimentation and ready access to medical professionals. Therefore, the acquisition of a comprehensive and suitable dataset remains an essential yet formidable challenge. Here, we propose a novel framework for achieving the automated acquisition of cardiac ultrasound datasets by controlling robot behaviour using a digital twin within a simulated environment. This framework consists of two modules: physical and virtual. Within the virtual simulation module, diverse body models of varying dimensions can be inputted, enabling the planning of robot arm path and ultrasound scanning manoeuvrers. Then the physical robot arm clones the actions of the robot in the simulation environment and updates its current state in the virtual module. The proposed framework was used to collect 43,000 cardiac ultrasound images from 8 patients with different pathologies and 1 healthy individual using a KUKA LBR Med robot and Intelligent Ultrasound Simulator. It is also expected to be feasible for a real-person dataset collection.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1877-0509
Funders:	CSC
Date of First Compliant Deposit:	11 December 2024
Date of Acceptance:	16 September 2024
Last Modified:	16 Dec 2024 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174683

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