In this work, we improve several steps of our Plasmon Assisted Microscopy Of Nano-sized Objects (PAMONO) sensor data processing pipeline through application of deep neural networks. The PAMONO-biosensor is a mobile nanoparticle sensor utilizing Surface Plasmon Resonance (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low Signal-to-Noise Rtion (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables the sensor to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop G RAPHICS P ROCESSING U NIT s (GPUs).

Conference Title

The 11th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2018)

Conference Country

Portugal

Conference Date

Jan. 19, 2018 - Jan. 21, 2018

Conference Sponsor

BIOSTEC