Image registration: Maximum likelihood, minimum entropy and deep learning.

Autor: Sedghi A; Medical Informatics Laboratory, Queen's University, Kingston, Canada. Electronic address: a.sedghi@queensu.ca., O'Donnell LJ; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA., Kapur T; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA., Learned-Miller E; College of Information and Computer Sciences, University of Massachusetts, Amherst, USA., Mousavi P; Medical Informatics Laboratory, Queen's University, Kingston, Canada., Wells WM 3rd; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2021 Apr; Vol. 69, pp. 101939. Date of Electronic Publication: 2020 Dec 18.
DOI: 10.1016/j.media.2020.101939
Abstrakt: In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
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 © 2020. Published by Elsevier B.V.)
Databáze: MEDLINE
Externí odkaz: