The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
arxiv(2021)
摘要
Registration of longitudinal brain MRI scans containing pathologies is
challenging due to dramatic changes in tissue appearance. Although there has
been progress in developing general-purpose medical image registration
techniques, they have not yet attained the requisite precision and reliability
for this task, highlighting its inherent complexity. Here we describe the Brain
Tumor Sequence Registration (BraTS-Reg) challenge, as the first public
benchmark environment for deformable registration algorithms focusing on
estimating correspondences between pre-operative and follow-up scans of the
same patient diagnosed with a diffuse brain glioma. The BraTS-Reg data comprise
de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated
for size and resolution according to a canonical anatomical template, and
divided into training, validation, and testing sets. Clinical experts annotated
ground truth (GT) landmark points of anatomical locations distinct across the
temporal domain. Quantitative evaluation and ranking were based on the Median
Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the
displacement field. The top-ranked methodologies yielded similar performance
across all evaluation metrics and shared several methodological commonalities,
including pre-alignment, deep neural networks, inverse consistency analysis,
and test-time instance optimization per-case basis as a post-processing step.
The top-ranked method attained the MEE at or below that of the inter-rater
variability for approximately 60
scope for further accuracy and robustness improvements, especially relative to
human experts. The aim of BraTS-Reg is to continue to serve as an active
resource for research, with the data and online evaluation tools accessible at
https://bratsreg.github.io/.
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