Related Experiment Video
Updated: May 12, 2026
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
Published on: October 27, 2023
Bayesian characterization of uncertainty in intra-subject non-rigid registration
Petter Risholm1, Firdaus Janoos, Isaiah Norton
1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. pettri@bwh.harvard.edu
Medical Image Analysis
|April 23, 2013
Summary
This study introduces a Bayesian non-rigid registration framework to quantify image registration uncertainty. The method estimates deformation and its uncertainty, crucial for high-level inferences from medical imaging data.
Area of Science:
- Medical image analysis
- Computational anatomy
- Bayesian inference
Background:
- Image registration uncertainty is vital for high-level inferences.
- Existing methods often provide single transformations, neglecting uncertainty.
Purpose of the Study:
- To propose a Bayesian non-rigid registration framework.
- To incorporate dissimilarity and regularization energies into a probabilistic model.
- To estimate both the most likely deformation and its associated uncertainty.
Main Methods:
- Utilized a Bayesian non-rigid registration framework.
- Employed Boltzmann's distribution for likelihood and prior.
- Characterized posterior distribution using Markov Chain Monte Carlo (MCMC).
- Marginalized hyper-parameters under uninformative hyper-priors.
Main Results:
- Successfully estimated maximum a posteriori (MAP) deformation and posterior uncertainty on synthetic data.
- Demonstrated that posterior distributions can be non-Gaussian.
- Applied to clinical neurosurgery data for brain tumor resection.
- Showed increased registration uncertainty at the resection site, with multi-modal marginal deformation distributions.
Conclusions:
- The proposed Bayesian framework effectively quantifies registration uncertainty.
- This uncertainty estimation is valuable for clinical applications, particularly in neurosurgery.
- The method reveals complex, non-Gaussian uncertainty patterns, especially near surgical sites.
Related Concept Videos
00:54Uncertainty: Confidence Intervals
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
01:19Interpretation of Confidence Intervals
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...