ArticleInternational journal of computer assisted radiology and surgery2016
Estimating FLEimage distributions of manual fiducial localization in CT images.
Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Reference-free Bayesian model for pointing errors of typein neurosurgical planning.International journal of computer assisted radiology and surgery · 2023Article
- Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Vector field analysis for surface registration in computer-assisted ENT surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2019Article
- Probe versus microscope: a comparison of different methods for image-to-patient registration.International journal of computer assisted radiology and surgery · 2018Article
- Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration.International journal of computer assisted radiology and surgery · 2018Article
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2 authors.
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Abstract
purposeThe fiducial localization error distribution (FLE) and fiducial configuration govern the application accuracy of point-based registration and drive target registration error (TRE) prediction models. The error of physically localizing patient fiducials ([Formula: see text]) is negligible when a registration probe matches the implanted screws with mechanical precision. Reliable trackers provide an unbiased estimate of the positional error ([Formula: see text]) with cheap repetitions. FLE further contains the localization error in the imaging data ([Formula: see text]), sampling of which in general is expensive and possibly biased. Finding the best techniques for estimating [Formula: see text] is crucial for the applicability of the TRE prediction methods.
methodsWe built a ground-truth (gt)-based unbiased estimator ([Formula: see text]) of [Formula: see text] from the samples collected in a virtual CT dataset in which the true locations of image fiducials are known by definition. Replacing true locations in [Formula: see text] by the sample mean creates a practical difference-to-mean (dtm)-based estimator ([Formula: see text]) that is applicable on any dataset. To check the practical validity of the dtm estimator, ten persons manually localized nine fiducials ten times in the virtual CT and the resulting [Formula: see text] and [Formula: see text] distributions were tested for statistical equality with a kernel-based two-sample test using the maximum mean discrepancy (MMD) (Gretton in J Mach Learn Res 13:723-773, 2012) statistics at [Formula: see text].
results[Formula: see text] and [Formula: see text] were found (for most of the cases) not to be statistically significantly different; conditioning them on persons and/or screws however yielded statistically significant differences much more often.
conclusionsWe conclude that [Formula: see text] is the best candidate (within our model) for estimating [Formula: see text] in homogeneous TRE prediction models. The presented approach also allows ground-truth-based numerical validation of [Formula: see text] estimators and (manual/automatic) image fiducial localization methods in phantoms with parameters similar to clinical datasets.
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