Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 7/2019

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

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

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 7/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs
next article EasyLabels: weak labels for scene segmentation in laparoscopic videos

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
1.
Adagolodjo Y, Trivisonne R, Haouchine N, Cotin S, Courtecuisse H (2017) Silhouette-based pose estimation for deformable organs application to surgical augmented reality. In: Intelligent robots and systems (IROS). IEEE, pp 539–544
2.
Allard J, Cotin S, Faure F, Bensoussan PJ, Poyer F, Duriez C, Delingette H, Grisoni L (2007) Sofa—an open source framework for medical simulation. In: MMVR 15-Medicine meets virtual reality, vol 125. IOP Press, pp 13–18
3.
Amberg B, Romdhani S, Vetter T (2007) Optimal step nonrigid ICP algorithms for surface registration. In: IEEE conference on CVPR’07. IEEE, pp 1–8
4.
Clements LW, Chapman WC, Dawant BM, Galloway RL Jr, Miga MI (2008) Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med Phys 35(6Part1):2528–2540CrossRefPubMedPubMedCentral
5.
Collins JA, Weis JA, Heiselman JS, Clements LW, Simpson AL, Jarnagin WR, Miga MI (2017) Improving registration robustness for image-guided liver surgery in a novel human-to-phantom data framework. IEEE Trans Med Imaging 36(7):1502–1510CrossRefPubMedPubMedCentral
6.
Collins T, Bartoli A (2015) [POSTER] Realtime shape-from-template: system and applications. In: 2015 IEEE international symposium on ISMAR. IEEE, pp 116–119
7.
Collins T, Bartoli A, Bourdel N, Canis M (2016) Robust, real-time, dense and deformable 3D organ tracking in laparoscopic videos. In: MICCAI. Springer, pp 404–412
8.
Collins T, Chauvet P, Debize C, Pizarro D, Bartoli A, Canis M, Bourdel N (2016) A system for augmented reality guided laparoscopic tumour resection with quantitative ex-vivo user evaluation. In: CARE. Springer, pp 114–126
9.
Cour T, Srinivasan P, Shi J (2006) Balanced graph matching. In: Proceedings of the 19th international conference on neural information processing systems. MIT Press, pp 313–320
10.
11.
Fabian S, Spinczyk D (2018) Target registration error minimization for minimally invasive interventions involving deformable organs. Comput Med Imaging Graph 65:4–10CrossRefPubMed
12.
Fitzpatrick JM (2009) Fiducial registration error and target registration error are uncorrelated. In: Medical imaging 2009—visualization, image-guided procedures, and modeling, vol 7261. International Society for Optics and Photonics, p 726102
13.
Haouchine N, Dequidt J, Peterlik I, Kerrien E, Berger MO, Cotin S (2013) Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In: 2013 IEEE international symposium on ISMAR. IEEE
14.
Haouchine N, Roy F, Untereiner L, Cotin S (2016) Using contours as boundary conditions for elastic registration during minimally invasive hepatic surgery. In: 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE
15.
Heiselman JS, Collins JA, Clements LW, Weis JA, Simpson AL, Geevarghese SK, Kingham TP, Jarnagin WR, Miga MI (2018) Nonrigid registration for laparoscopic liver surgery using sparse intraoperative data. In: Medical imaging 2018—image-guided procedures, robotic interventions, and modeling, vol 10576. International Society for Optics and Photonics, p 105760D
16.
Kerdok AE, Cotin SM, Ottensmeyer MP, Galea AM, Howe RD, Dawson SL (2003) Truth cube: establishing physical standards for soft tissue simulation. Med Image Anal 7(3):283–291CrossRefPubMed
17.
Klosowski JT, Held M, Mitchell JS, Sowizral H, Zikan K (1998) Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE TVCG 4:21–36
18.
Koo B, Özgür E, Le Roy B, Buc E, Bartoli A (2017) Deformable registration of a preoperative 3D liver volume to a laparoscopy image using contour and shading cues. In: MICCAI. Springer, pp 326–334
19.
Mahmoud N, Cirauqui I, Hostettler A, Doignon C, Soler L, Marescaux J, Montiel J (2016) Orbslam-based endoscope tracking and 3D reconstruction. In: CARE. Springer, pp 72–83
20.
Modrzejewski R, Collins T, Bartoli A, Hostettler A, Marescaux J (2018) Soft-body registration of pre-operative 3D models to intra-operative RGBD partial body scans. In: MICCAI. Springer, pp 39–46
21.
Plantefeve R, Peterlik I, Haouchine N, Cotin S (2016) Patient-specific biomechanical modeling for guidance during minimally-invasive hepatic surgery. Ann Biomed Eng 44(1):139–153CrossRefPubMed
22.
Rumman NA, Schaerf M, Bechmann D (2015) Collision detection for articulated deformable characters. In: Proceedings of the 8th ACM SIGGRAPH conference on motion in games. ACM, pp 215–220
23.
Suwelack S, Röhl S, Bodenstedt S, Reichard D, Dillmann R, Santos T, Maier-Hein L, Wagner M, Wünscher J, Kenngott H (2014) Physics-based shape matching for intraoperative image guidance. Med Phys 41(11):111901CrossRefPubMed
24.
Thompson S, Schneider C, Bosi M, Gurusamy K, Ourselin S, Davidson B, Hawkes D, Clarkson MJ (2018) In vivo estimation of target registration errors during augmented reality laparoscopic surgery. CARS 13(6):865–874CrossRef
25.
Thompson S, Totz J, Song Y, Johnsen S, Stoyanov D, Ourselin S, Gurusamy K, Schneider C, Davidson B, Hawkes D (2015) Accuracy validation of an image guided laparoscopy system for liver resection. In: Medical imaging 2015—image-guided procedures, robotic interventions, and modeling, vol 9415. SPIE, p 941509
26.
Zijlmans M, Langø T, Hofstad EF, Van Swol CF, Rethy A (2012) Navigated laparoscopy–liver shift and deformation due to pneumoperitoneum in an animal model. Minim Invasive Ther Allied Technol 21(3):241–248CrossRefPubMed
Metadata
Title
An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions
Authors
Richard Modrzejewski
Toby Collins
Barbara Seeliger
Adrien Bartoli
Alexandre Hostettler
Jacques Marescaux
Publication date
30-05-2019
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 7/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-02001-4

Other articles of this Issue 7/2019

International Journal of Computer Assisted Radiology and Surgery 7/2019 Go to the issue

Original Article

Extending pretrained segmentation networks with additional anatomical structures

Original Article

A novel gaze-supported multimodal human–computer interaction for ultrasound machines

Original Article

Design optimization of a contact-aided continuum robot for endobronchial interventions based on anatomical constraints

Original Article

High-precision evaluation of electromagnetic tracking

Original Article

A “pickup” stereoscopic camera with visual-motor aligned control for the da Vinci surgical system: a preliminary study

Original Article

Pedicle screw navigation using surface digitization on the Microsoft HoloLens

Premium Partner

    Image Credits