Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

Yiming Xiao; Andreas Maier; Wolfgang Wein; Roozbeh Shams; Samuel Kadoury; David Drobny; Marc Modat; Ingerid Reinertsen; Hassan Rivaz; Matthieu Chabanas; Maryse Fortin; Ines MacHado; Yangming Ou; Mattias P. Heinrich; Julia A. Schnabel; Xia Zhong

doi:10.1109/TMI.2019.2935060

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

Yiming Xiao^*, Andreas Maier, Wolfgang Wein, Roozbeh Shams, Samuel Kadoury, David Drobny, Marc Modat, Ingerid Reinertsen, Hassan Rivaz, Matthieu Chabanas, Maryse Fortin, Ines MacHado, Yangming Ou, Mattias P. Heinrich, Julia A. Schnabel, Xia Zhong

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Medizinische Informatik

71 Zitate (Scopus)

Abstract

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 pre-surgical 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.

Originalsprache	Englisch
Aufsatznummer	8795512
Zeitschrift	IEEE Transactions on Medical Imaging
Jahrgang	39
Ausgabenummer	3
Seiten (von - bis)	777-786
Seitenumfang	10
ISSN	0278-0062
DOIs	https://doi.org/10.1109/TMI.2019.2935060
Publikationsstatus	Veröffentlicht - 03.2020

Fördermittel

Manuscript received July 15, 2019; accepted August 6, 2019. Date of publication August 13, 2019; date of current version February 28, 2020. This work was supported by the Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, NSERC Discovery, under Grant RGPIN-2015-04136, and in part by the French ANR within the Investissements d’Avenir Program under Grant ANR-11-LABX-0004 (Labex CAMI). The work of Y. Xiao was supported by BrainsCAN and CIHR Postdoctoral Fellowship. The work of M. P. Heinrich was funded in part by the German Research Foundation (DFG) under Grant 320997906 HE 7364/2-1. The work of D. Drobny was supported in part by the UCL EPSRC Centre for Doctoral Training in Medical Imaging, in part by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences under Grant NS/A000050/1, and in part by the Wellcome/EPSRC Centre for Medical Engineering under Grant WT 203148/Z/16/Z and EPSRC under Grant NS/A000027/1. (Corresponding author: Yiming Xiao.) Y. Xiao is with the Robarts Research Institute, Western University, London, ON N6A 5B7, Canada (e-mail: [email protected]).

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10.1109/TMI.2019.2935060

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Xiao, Y., Maier, A., Wein, W., Shams, R., Kadoury, S., Drobny, D., Modat, M., Reinertsen, I., Rivaz, H., Chabanas, M., Fortin, M., MacHado, I., Ou, Y., Heinrich, M. P., Schnabel, J. A., & Zhong, X. (2020). Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge. IEEE Transactions on Medical Imaging, 39(3), 777-786. Artikel 8795512. https://doi.org/10.1109/TMI.2019.2935060

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title = "Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge",

abstract = "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 pre-surgical 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.",

author = "Yiming Xiao and Andreas Maier and Wolfgang Wein and Roozbeh Shams and Samuel Kadoury and David Drobny and Marc Modat and Ingerid Reinertsen and Hassan Rivaz and Matthieu Chabanas and Maryse Fortin and Ines MacHado and Yangming Ou and Heinrich, \{Mattias P.\} and Schnabel, \{Julia A.\} and Xia Zhong",

note = "Funding Information: Manuscript received July 15, 2019; accepted August 6, 2019. Date of publication August 13, 2019; date of current version February 28, 2020. This work was supported by the Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, NSERC Discovery, under Grant RGPIN-2015-04136, and in part by the French ANR within the Investissements d{\textquoteright}Avenir Program under Grant ANR-11-LABX-0004 (Labex CAMI). The work of Y. Xiao was supported by BrainsCAN and CIHR Postdoctoral Fellowship. The work of M. P. Heinrich was funded in part by the German Research Foundation (DFG) under Grant 320997906 HE 7364/2-1. The work of D. Drobny was supported in part by the UCL EPSRC Centre for Doctoral Training in Medical Imaging, in part by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences under Grant NS/A000050/1, and in part by the Wellcome/EPSRC Centre for Medical Engineering under Grant WT 203148/Z/16/Z and EPSRC under Grant NS/A000027/1. (Corresponding author: Yiming Xiao.) Y. Xiao is with the Robarts Research Institute, Western University, London, ON N6A 5B7, Canada (e-mail: [email protected]). Publisher Copyright: {\textcopyright} 1982-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

month = mar,

doi = "10.1109/TMI.2019.2935060",

language = "English",

volume = "39",

pages = "777--786",

journal = "IEEE Transactions on Medical Imaging",

issn = "0278-0062",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Xiao, Y, Maier, A, Wein, W, Shams, R, Kadoury, S, Drobny, D, Modat, M, Reinertsen, I, Rivaz, H, Chabanas, M, Fortin, M, MacHado, I, Ou, Y, Heinrich, MP, Schnabel, JA & Zhong, X 2020, 'Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge', IEEE Transactions on Medical Imaging, Jg. 39, Nr. 3, 8795512, S. 777-786. https://doi.org/10.1109/TMI.2019.2935060

TY - JOUR

T1 - Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

AU - Xiao, Yiming

AU - Maier, Andreas

AU - Wein, Wolfgang

AU - Shams, Roozbeh

AU - Kadoury, Samuel

AU - Drobny, David

AU - Modat, Marc

AU - Reinertsen, Ingerid

AU - Rivaz, Hassan

AU - Chabanas, Matthieu

AU - Fortin, Maryse

AU - MacHado, Ines

AU - Ou, Yangming

AU - Heinrich, Mattias P.

AU - Schnabel, Julia A.

AU - Zhong, Xia

N1 - Funding Information: Manuscript received July 15, 2019; accepted August 6, 2019. Date of publication August 13, 2019; date of current version February 28, 2020. This work was supported by the Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, NSERC Discovery, under Grant RGPIN-2015-04136, and in part by the French ANR within the Investissements d’Avenir Program under Grant ANR-11-LABX-0004 (Labex CAMI). The work of Y. Xiao was supported by BrainsCAN and CIHR Postdoctoral Fellowship. The work of M. P. Heinrich was funded in part by the German Research Foundation (DFG) under Grant 320997906 HE 7364/2-1. The work of D. Drobny was supported in part by the UCL EPSRC Centre for Doctoral Training in Medical Imaging, in part by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences under Grant NS/A000050/1, and in part by the Wellcome/EPSRC Centre for Medical Engineering under Grant WT 203148/Z/16/Z and EPSRC under Grant NS/A000027/1. (Corresponding author: Yiming Xiao.) Y. Xiao is with the Robarts Research Institute, Western University, London, ON N6A 5B7, Canada (e-mail: [email protected]). Publisher Copyright: © 1982-2012 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/3

Y1 - 2020/3

N2 - 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 pre-surgical 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.

AB - 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 pre-surgical 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.

UR - https://www.scopus.com/pages/publications/85076691087

U2 - 10.1109/TMI.2019.2935060

DO - 10.1109/TMI.2019.2935060

M3 - Journal articles

C2 - 31425023

AN - SCOPUS:85076691087

SN - 0278-0062

VL - 39

SP - 777

EP - 786

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 3

M1 - 8795512

ER -

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge

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