Deep Learning Method for Probabilistic Particle Detection and Tracking in Fluorescence Microscopy Images | IEEE Conference Publication | IEEE Xplore

Deep Learning Method for Probabilistic Particle Detection and Tracking in Fluorescence Microscopy Images


Abstract:

Analyzing particles in fluorescence microscopy images is important to obtain insights into viral and cellular processes. We introduce a deep learning method for probabili...Show More

Abstract:

Analyzing particles in fluorescence microscopy images is important to obtain insights into viral and cellular processes. We introduce a deep learning method for probabilistic detection and tracking of fluorescent particles. For particle detection, we integrate temporal information for regressing a density map and determine sub-pixel particle positions. Detections close to particles are rewarded during training and highly nonlinear direct regression of positions is avoided. For tracking, we introduce a fully Bayesian neural network that emulates classical Bayesian filtering and exploits both aleatoric and epistemic uncertainty. The method considers uncertainty information of individual particle detections. Experiments based on the Particle Tracking Challenge data show that the proposed method outperforms previous methods. We also applied the method to fluorescence microscopy images of hepatitis C virus proteins.
Date of Conference: 18-21 April 2023
Date Added to IEEE Xplore: 01 September 2023
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Conference Location: Cartagena, Colombia

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