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SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery

Montaña-Brown, N; Saeed, SU; Abdulaal, A; Dowrick, T; Kilic, Y; Wilkinson, S; Gao, J; ... Clarkson, MJ; + view all (2023) SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery. In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). NeurIPS Green open access

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Abstract

Minimally-invasive surgery (MIS) and robot-assisted minimally invasive (RAMIS) surgery offer well-documented benefits to patients such as reduced post-operative pain and shorter hospital stays. However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data acquisition and curation, partially caused by the ethical considerations of training, testing and deploying AI models in medical environments. We introduce SARAMIS, the first large-scale dataset of anatomically derived 3D rendering assets of the human abdominal anatomy. Using previously existing, open-source CT datasets of the human anatomy, we derive novel 3D meshes, tetrahedral volumes, textures and diffuse maps for over 104 different anatomical targets in the human body, representing the largest, open-source dataset of 3D rendering assets for synthetic simulation of vision tasks in MIS+RAMIS, increasing the availability of openly available 3D meshes in the literature by three orders of magnitude. We supplement our dataset with a series of GPU-enabled rendering environments, which can be used to generate datasets for realistic MIS/RAMIS tasks. Finally, we present an example of the use of SARAMIS assets for an autonomous navigation task in colonoscopy from CT abdomen-pelvis scans for the first time in the literature. SARAMIS is publically made available at https://github.com/NMontanaBrown/saramis/, with assets released under a CC-BY-NC-SA license.

Type: Proceedings paper
Title: SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
Event: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.
Open access status: An open access version is available from UCL Discovery
Publisher version: https://proceedings.neurips.cc/paper_files/paper/2...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
URI: https://discovery.ucl.ac.uk/id/eprint/10191576
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