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Verfasst von:Spilger, Roman [VerfasserIn]   i
 Lee, Ji Young [VerfasserIn]   i
 Chagin, Vadim O. [VerfasserIn]   i
 Schermelleh, Lothar [VerfasserIn]   i
 Cardoso, M. Cristina [VerfasserIn]   i
 Bartenschlager, Ralf [VerfasserIn]   i
 Rohr, Karl [VerfasserIn]   i
Titel:Deep probabilistic tracking of particles in fluorescence microscopy images
Verf.angabe:Roman Spilger, Ji-Young Lee, Vadim O. Chagin, Lothar Schermelleh, M. Cristina Cardoso, Ralf Bartenschlager, Karl Rohr
E-Jahr:2021
Jahr:8 June 2021
Umfang:18 S.
Teil:volume:72
 year:2021
 elocationid:102128
 pages:1-18
 extent:18
Fussnoten:Gesehen am 15.10.2021
Titel Quelle:Enthalten in: Medical image analysis
Ort Quelle:Amsterdam [u.a.] : Elsevier Science, 1996
Jahr Quelle:2021
Band/Heft Quelle:72(2021), Artikel-ID 102128, Seite 1-18
ISSN Quelle:1361-8423
Abstract: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.
DOI:doi:10.1016/j.media.2021.102128
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.media.2021.102128
 Volltext: https://www.sciencedirect.com/science/article/pii/S1361841521001742
 DOI: https://doi.org/10.1016/j.media.2021.102128
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Biomedical imaging
 Deep learning
 Microscopy images
 Tracking
K10plus-PPN:1774132893
Verknüpfungen:→ Zeitschrift

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