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.

鉴于图像配准在医学领域的临床和研究领域都是一项基本且普遍的任务，因此配准错误可能会产生严重后果。由于此类错误可能会在图像引导治疗期间误导临床医生或使下游分析的结果产生偏差，因此估计配准错误的方法变得越来越流行。为了给这个新的异质领域提供结构，我们开发了一种分类法，并对定量和自动提供配准误差的密集估计的方法进行了范围审查。分类法将误差估计方法分解为方法（基于图像或转换）、框架（机器学习或直接）和测量（误差或置信度）组件。遵循 PRISMA 范围审查指南，发现的 570 条记录减少到 20 条符合纳入标准的研究，然后根据拟议的分类法对其进行审查。还讨论了该领域的趋势、方法的优缺点以及潜在的偏差来源。我们为最佳实践提供建议并确定未来研究的领域。