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Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors

Ramalhinho, J; Tregidgo, H; Allam, M; Travlou, N; Gurusamy, K; Davidson, B; Hawkes, D; ... Clarkson, M; + view all (2019) Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors. In: Wang, Q and Gomez, A and Hutter, J and McLeod, K and Zimmer, V and Zettinig, O and Licandro, R and Robinson, E and Christiaens, D and Abaci Turk, E and Melbourne, A, (eds.) Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. (pp. pp. 11-19). Springer: Shenzhen, China. Green open access

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Abstract

Laparoscopic Ultrasound (LUS) can enhance the safety of laparoscopic liver resection by providing information on the location of major blood vessels and tumours. Since many tumours are not visible in ultrasound, registration to a pre-operative CT has been proposed as a guidance method. In addition to being multi-modal, this registration problem is greatly affected by the differences in field of view between CT and LUS, and thus requires an accurate initialisation. We propose a novel method of registering smaller field of view slices to a larger volume globally using a Content-based retrieval framework. This problem is under-constrained for a single slice registration, resulting in non-unique solutions. Therefore, we introduce kinematic priors in a Bayesian framework in order to jointly register groups of ultrasound images. Our method then produces an estimate of the most likely sequence of CT images to represent the ultrasound acquisition and does not require tracking information nor an accurate initialisation. We demonstrate the feasibility of this approach in multiple LUS acquisitions taken from three sets of clinical data.

Type: Proceedings paper
Title: Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors
Event: First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32875-7_2
Publisher version: https://doi.org/10.1007/978-3-030-32875-7_2
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Laparoscopic Ultrasound · Multi-modal Registration · Bayesian models.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
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 > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10086925
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