Browsing by Subject "Fluorescence imaging"

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    Deep learning-driven adaptive optics for single-molecule localization microscopy
    (Springer Nature, 2023) Zhang, Peiyi; Ma, Donghan; Cheng, Xi; Tsai, Andy P.; Tang, Yu; Gao, Hao-Cheng; Fang, Li; Bi, Cheng; Landreth, Gary E.; Chubykin, Alexander A.; Huang, Fang; Anatomy, Cell Biology and Physiology, School of Medicine
    The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.
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    DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data
    (Nature Research, 2019-12-04) Dunn, Kenneth W.; Fu, Chichen; Ho, David Joon; Lee, Soonam; Han, Shuo; Salama, Paul; Delp, Edward J.; Medicine, School of Medicine
    The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.