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

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-sc...