A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted Laparoscopic Liver Resection

ABSTRACT

Augmented Reality (AR) can assist Laparoscopic Liver Resection (LLR) by registering a preoperative 3D model with laparoscopic images. Evaluating the accuracy of the registration methods requires measuring Target Registration Error (TRE). Previous work evaluates TRE on simulated, phantom and animal data but not on clinical data. Our contribution is a methodology for groundtruth acquisition using Laparoscopic Ultrasound (LUS) in clinical LLR, two evaluation criteria, a four-patient dataset and an evaluation of two existing registration methods. The LUS is coregistered with the laparoscope and its observations are made transferable to 3D laparoscope coordinates. The average position error in our LUS registered images is estimated to be about 1 mm, which is far better than the measured errors for the state-of-the-art registration methods, making our dataset relevant for their evaluation. We ran a preliminary evaluation of two registration methods. The dataset is available publicly at http://igt.ip.uca.fr/ab/code_and_datasets/datasets/llr_reg_evaluation_by_lus.

KEYWORDS:

• Computer-assisted intervention
• Laparoscopic Liver Resection
• augmented reality
• Target Registration Error
• Ultrasound probe calibration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partly funded by CNRS under the Hepataug pre-industrial project and by Cancéropôle CLARA under the AIALO project.

Notes on contributors

N. Rabbani

Navid Rabbani has been working as a post-doc researcher in  Endoscopy and Computer Vision (EnCoV) research group at Université Clermont Auvergne since 2019. His main research interests are in computer vision and machine learning and their application to medical images.

L. Calvet

Lilian Calvet was research associate at the University and Hospital of Clermont-Ferrand and member of the Endoscopy and Computer Vision (EnCoV) research group since fall 2016. He is postdoctoral fellow at Université de Toulouse since fall 2020. His research interests are in computer vision and their application to computer-aided medical interventions and post-production.

Y. Espinel

Yamid Espinelis a PhD student at Université Clermont-Auvergne, France, working on 3D-2D Automatic Registration for Augmented Reality in Laparoscopic Surgery of Liver. He has a bachelor's degree in Electronic Engineering from the University of Ibague, Colombia, and a master degree in Computer Vision from the University of Burgundy, France. His main research interests include Computer Vision, Mathematical Optimization, Machine Learning and  Computer-Assisted Medical Interventions.

B. Le Roy

Bertrand Le Roy is a Professor in digestive surgery and head of digestive and oncologic surgery department at centre hospitalier universitaire de Saint-Etienne. He is author and co-author in more than 70 publications.

M. Ribeiro

Mathieu Ribeiro has been serving his residency in visceral surgery at the University Hospital of Clermont Ferrand in France. Currently he studies a Master's in “Life Sciences and Health” in Paris-Saclay University. His research interest is about augmented reality in laparoscopic hepatic surgery.

E. Buc

Emmanuel Buc has held the position of Professor of visceral surgery at Université Clermont Auvergne since 2014. He is an hepatic surgeon at the centre hospitalier universitaire de Clermont-Ferrand and works in the field of the augmented reality with the Endoscopy and Computer Vision (EnCoV) research group since 2016.

A. Bartoli

Adrien Bartoli has held the position of Professor of Computer Science at Université Clermont Auvergne since fall 2009 and has been a member of Institut Universitaire de France since 2016. He is currently on leave as research scientist at the University Hospital of Clermont-Ferrand and as Chief Scientific Officer at SurgAR. He leads the Endoscopy and Computer Vision (EnCoV) research group at the University and Hospital of Clermont-Ferrand. His main research interests are in computer vision, including image registration and Shape-from-X for rigid and deformable environments, and their application to computer-aided medical interventions.