Til hovedinnhold

Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge

Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge

Kategori
Tidsskriftspublikasjon
Sammendrag
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the presurgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.
Språk
Engelsk
Forfatter(e)
  • Xiao Yiming
  • Rivaz Hassan
  • Chabanas Matthieu
  • Fortin Maryse
  • Machado Ines
  • Ou Yangming
  • Heinrich Matthias P.
  • Schnabel Julia
  • Zhong Xia
  • Maier Andreas
  • Wein Wolfgang
  • Shams Roozbeh
  • Kadoury Samuel
  • Drobny David
  • Modat Marc
  • Reinertsen Ingerid
Institusjon(er)
  • Storbritannia og Nord-Irland
  • Concordia University
  • Université Grenoble Alpes
  • Harvard Medical School
  • Universität zu Lübeck
  • King's College London, University of London
  • Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Tyskland
  • Ecole polytechnique de Montréal
  • SINTEF Digital / Helse
År
2019
Publisert i
IEEE Transactions on Medical Imaging
ISSN
0278-0062