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

TL;DR

This paper evaluates MRI to ultrasound registration methods for brain shift correction during surgery, benchmarking six automated algorithms on clinical datasets to improve intra-operative imaging accuracy.

Contribution

It presents a comprehensive benchmark of MRI-iUS registration algorithms from multiple research groups using a standardized challenge dataset.

Findings

All algorithms were trained on the RESECT database.

Algorithms were ranked based on performance on a test dataset.

Insights and future directions for brain shift correction are discussed.

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 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.