Name
Title | ||
|---|---|---|
Bayesian inference for uncertainty quantification in point-based deformable image registration. |
Abstract | ||
|---|---|---|
In image guided diagnostics the treatment of patients is often decided based on registered image data. During the registration process errors can occur, e.g., due to incorrect model assumptions or non-corresponding areas due to image artifacts or pathologies. Therefore, the study of approaches that analyze the accuracy and reliability of registration results has become increasingly important in recent years. One way to quantify registration uncertainty is based on the posterior distribution of the transformation parameters. Since the exact computation of the posterior distribution is intractable, variational Bayes inference can be used to efficiently provide an approximate solution. Recently, a probabilistic approach to intensity-based registration has been developed that uses sparse point-based representations of images and shows an intrinsic ability to deal with corrupted data. A natural output are correspondence probabilities between the two point sets which provide a measure for potentially non-corresponding and thus incorrectly deformed regions. In order to perform a comparative analysis of registration uncertainty and correspondence probabilities, we integrate a nonlinear point-based probabilistic registration method in a variational Bayesian framework. The developed method is applied to MR images with brain lesions, where both measures show moderate correlations, but a different behavior with respect to altered regularization. Further, we simulate realistic ground-truth data to allow for a correlation analysis between both measures and local registration errors. In fact, registration errors due to model differences cannot be depicted by registration uncertainty, however, in the presence of corrupted image areas, a strong correlation can be found. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2512988 | Proceedings of SPIE |
Field | DocType | Volume |
Uncertainty quantification,Bayesian inference,Pattern recognition,Computer science,Artificial intelligence,Image registration | Conference | 10949 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandra Schultz | 1 | 4 | 2.11 |
| Julia Krüger | 2 | 16 | 7.63 |
| Heinz Handels | 3 | 1527 | 239.84 |
| Jan Ehrhardt | 4 | 387 | 54.33 |