Estimating medical image registration error and confidence: A taxonomy and scoping review.

Autor: Bierbrier J; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada. Electronic address: joshua.bierbrier@mail.mcgill.ca., Gueziri HE; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada., Collins DL; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2022 Oct; Vol. 81, pp. 102531. Date of Electronic Publication: 2022 Jul 06.
DOI: 10.1016/j.media.2022.102531
Abstrakt: Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022. Published by Elsevier B.V.)
Databáze: MEDLINE
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