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.
| Original language | English |
|---|---|
| Article number | 8795512 |
| Journal | IEEE Transactions on Medical Imaging |
| Volume | 39 |
| Issue number | 3 |
| Pages (from-to) | 777-786 |
| Number of pages | 10 |
| ISSN | 0278-0062 |
| DOIs | |
| Publication status | Published - 03.2020 |
Funding
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]).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 9 Industry, Innovation, and Infrastructure
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