Deep probabilistic tracking of particles in fluorescence microscopy images
| Autor: | Lothar Schermelleh, Roman Spilger, Ralf Bartenschlager, Karl Rohr, Vadim O. Chagin, M. Cristina Cardoso, Ji-Young Lee |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Reliability (computer networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Health Informatics Tracking (particle physics) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Humans Radiology Nuclear Medicine and imaging Uncertainty quantification Ground truth Radiological and Ultrasound Technology Artificial neural network business.industry Deep learning Probabilistic logic Reproducibility of Results Pattern recognition Bayes Theorem Computer Graphics and Computer-Aided Design Recurrent neural network Microscopy Fluorescence Computer Vision and Pattern Recognition Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Medical image analysis. 72 |
| ISSN: | 1361-8423 |
| Popis: | Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods. |
| Databáze: | OpenAIRE |
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