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International Journal of Computer Assisted Radiology and Surgery

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30.05.2019 | Original Article

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

verfasst von: Richard Modrzejewski, Toby Collins, Barbara Seeliger, Adrien Bartoli, Alexandre Hostettler, Jacques Marescaux

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2019

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Abstract

Purpose

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.

Method

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.

Results

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.

Conclusion

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.

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Titel: An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions
verfasst von: Richard Modrzejewski
Toby Collins
Barbara Seeliger
Adrien Bartoli
Alexandre Hostettler
Jacques Marescaux
Publikationsdatum: 30.05.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-02001-4

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

Abstract

Purpose

Method

Results

Conclusion

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Purpose

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