A global method for non-rigid registration of cell nuclei in live cell time-lapse images

• Verfasst von: Gao, Qi [VerfasserIn]
• Verfasst von: Rohr, Karl [VerfasserIn]
• Titel: A global method for non-rigid registration of cell nuclei in live cell time-lapse images
• Verf.angabe: Qi Gao, Karl Rohr
• E-Jahr: 2019
• Jahr: 27 February 2019
• Umfang: 12 S.
• Fussnoten: Gesehen am 09.12.2019
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on medical imaging
• Ort Quelle: New York, NY : Institute of Electrical and Electronics Engineers,, 1982
• Jahr Quelle: 2019
• Band/Heft Quelle: 38(2019), 10, Seite 2259-2270
• ISSN Quelle: 1558-254X
• DOI: doi:10.1109/TMI.2019.2901918
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Biomedical optical imaging
• Sach-SW: Computational modeling
• Sach-SW: fluorescence microscopy images
• Sach-SW: Markov random field
• Sach-SW: Microscopy
• Sach-SW: Non-rigid registration
• Sach-SW: optical flow
• Sach-SW: Optical imaging
• Sach-SW: Optical microscopy
• Sach-SW: Optical scattering
• Sach-SW: regularization
• Sach-SW: restricted Boltzmann machine
• Sach-SW: Strain
• K10plus-PPN: 1684866405

Abstract

Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in the existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. We compared different regularizers for the deformation fields and found that a convex quadratic function is more suitable than non-convex ones. To improve the robustness, we propose an adaptive weighting scheme based on the statistics of the noise in fluorescence microscopy images as well as a combined local-global scheme. Moreover, we extend the global method by exploiting high-order image features. The best suitable high-order features are determined through learning two generative image models, namely, fields of experts and convolutional Gaussian restricted Boltzmann machine, whose model formulations are both consistent with the assumption of high-order feature constancy in the registration model. Using multiple data sets of real 2D and 3D live cell microscopy image sequences as well as synthetic image data, we demonstrate that our proposed approach outperforms the previous methods in terms of both registration accuracy and computational efficiency.