We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching.
View Article and Find Full Text PDFMicrotubules (MTs) play critical roles in neuronal development, but many questions remain about the molecular mechanisms of their regulation and function. Furthermore, despite progress in understanding postsynaptic MTs, much less is known about the contributions of presynaptic MTs to neuronal morphogenesis. In particular, studies of in vivo MT dynamics in Drosophila sensory dendrites yielded significant insights into polymer-level behavior.
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