TY - JOUR T1 - Spectral and lifetime fluorescence unmixing via deep learning JF - bioRxiv DO - 10.1101/745216 SP - 745216 AU - Jason T. Smith AU - Marien Ochoa AU - Pingkun Yan AU - Xavier Intes Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/08/24/745216.abstract N2 - Hyperspectral Fluorescence Lifetime Imaging allows for the simultaneous acquisition of spectrally-resolved temporal fluorescence emission decays. In turn, the rich multidimensional data set acquired enables to image simultaneously multiple fluorescent species to facilitate high-content molecular imaging for improved diagnosis. However, to enable quantitative imaging, inherent spectral overlap between the considered fluorescent probes and potential bleed-through has to be taken into account. Such task is performed typically either via spectral or lifetime unmixing, but neither both simultaneously. Herein, we present UNMIX-ME, a deep learning fluorescence un-mixing algorithm that is tasked with performing quantitative fluorophore unmixing using both spectral and temporal signatures simultaneously. UNMIX-ME was efficiently trained and validated using an in silico framework replicating the characteristics of our compressive hyperspectral fluorescent lifetime imaging platform. It was benchmarked against a conventional LSQ method for tri-exponential simulated samples. Last, UNMIX-ME performances were assessed using NIR FRET in vitro and in vivo small animal experimental data. ER -