An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions

  • Richard ModrzejewskiEmail author
  • Toby Collins
  • Barbara Seeliger
  • Adrien Bartoli
  • Alexandre Hostettler
  • Jacques Marescaux
Original Article



The registration of preoperative 3D images to intra-operative laparoscopic 2D images is one of the main concerns for augmented reality in computer-assisted surgery. For laparoscopic liver surgery, while several algorithms have been proposed, there is neither a public dataset nor a systematic evaluation methodology to quantitatively evaluate registration accuracy.


Our main contribution is to provide such a dataset with an in vivo porcine model. It is used to evaluate a state-of-the-art registration algorithm that is capable of simultaneous registration and soft-body collision reasoning.


The dataset consists of 13 deformed liver states, with corresponding exploration videos and interventional CT acquisitions with 60 small artificial fiducials located on the surface of the liver and distributed within the parenchyma, where a precise registration is crucial for augmented reality. This dataset will be made public. Using this dataset, we show that collision reasoning improves performance of registration for strong deformation and independent lobe motion.


This dataset addresses the lack of public datasets in this field. As an example of use, we present and evaluate a state-of-the-art energy-based approach and a novel extension that handles self-collisions.


Evaluation dataset Deformable registration Augmented reality 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution at which the studies were conducted.


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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.EnCoV, Institut Pascal, UMR 6602, CNRS/UBP/SIGMAClermont-FerrandFrance
  2. 2.IRCAD and IHU-StrasbourgStrasbourgFrance

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