Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures

• Published in: Medical image analysis Vol. 97; p. 103227
• Main Authors: Ritter, C., Lee, J.-Y., Pham, M.-T., Pabba, M.K., Cardoso, M.C., Bartenschlager, R., Rohr, K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.10.2024
• Subjects: Algorithms; Bayes Theorem; Biomedical imaging; Chromatin; Covariance intersection algorithm; Deep Learning; Hepacivirus; Humans; Image Processing, Computer-Assisted - methods; Microscopy images; Microscopy, Fluorescence - methods; Multi-detector data fusion; Particle detection and tracking; Multi-detector data fusion; Microscopy images; Covariance intersection algorithm; Biomedical imaging; Particle detection and tracking
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103227

Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial–temporal resolutions. We found that the proposed approach outperforms existing methods. [Display omitted] •Probabilistic particle tracking by multi-detector fusion and Bayesian smoothing.•Detections from multiple detectors fused by intensity-based covariance intersection.•Multi-detector fusion exploits image intensities, positions, and uncertainties.•Information from future time points integrated by Bayesian smoothing.•Quantitative evaluation using microscopy images of viral and chromatin structures.