{"id":1936,"date":"2022-06-28T22:11:53","date_gmt":"2022-06-29T02:11:53","guid":{"rendered":"https:\/\/sites.bu.edu\/tianlab\/?p=1936"},"modified":"2022-07-05T22:14:10","modified_gmt":"2022-07-06T02:14:10","slug":"chang-and-qianwan-present-posters-at-sculpted-light-in-the-brain","status":"publish","type":"post","link":"https:\/\/sites.bu.edu\/tianlab\/2022\/06\/28\/chang-and-qianwan-present-posters-at-sculpted-light-in-the-brain\/","title":{"rendered":"Chang and Qianwan present posters at Sculpted Light in the Brain"},"content":{"rendered":"

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DeepVID: A Self-supervised Deep Learning Framework for Two-photon Voltage Imaging Denoising | Chang Liu<\/span><\/a><\/h4>\n<\/div>\n
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Voltage imaging is an evolving tool to continuously image neuronal activities for large number of neurons. Recently, a high-speed low-light two-photon voltage imaging framework was developed, which enabled kilohertz-scanning on population-level neurons in the awake behaving animal. However, with a high frame rate and a large field-of-view (FOV), shot noise dominates pixel-wise measurements and the neuronal signals are difficult to be identified in the single-frame raw measurement. Another issue is that although deep-learning-based methods has exhibited promising results in image denoising, the traditional supervised learning is not applicable to this problem as the lack of ground-truth \u201cclean\u201d (high SNR) measurements. To address these issues, we developed a self-supervised deep learning framework for voltage imaging denoising (DeepVID) without the need for any ground-truth data. Inspired by previous self-supervised algorithms, DeepVID infers the underlying fluorescence signal based on the independent temporal and spatial statistics of the measurement that is attributed to shot noise. DeepVID reduced the frame-to-frame variably of the image and achieved a 15-fold improvement in SNR when comparing denoised and raw image data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Computational Miniature Mesoscope V2 for single-shot 3D high-resolution fluorescence imaging | Qianwan Yang<\/span><\/a><\/h4>\n<\/div>\n
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Conventional microscopes are inherently constrained by its space-bandwidth product, which means compromises must be made to obtain either a low spatial resolution or a narrow field-of-view. Computational Miniature Mesoscope (CM2) is a novel fluorescence imaging device that overcomes this bottleneck by jointly designing the optics and algorithm. The CM2 platform achieves single-shot large-scale volumetric imaging with single cell resolution on a compact platform. Here, we demonstrate CM2 V2 \u2013 an advanced CM2 system that integrates novel hardware improvements and a new deep learning reconstruction framework. On the hardware side, the platform features a 3D-printed freeform illuminator that achieves ~80% excitation efficiency \u2013 a ~3X improvement over our V1 design, and a hybrid emission filter design that improves the measurement contrast by >5X. On the computational side, the new proposed computational pipeline, termed CM2Net, is fueled by simulated realistic field varying data to perform fast and reliable 3D reconstruction. As compared to the model-based deconvolution in our V1 system, CM2Net achieves ~8X better axial localization and ~1400X faster reconstruction speed. The trained CM2Net is validated by imaging phantom objects with embedded fluorescent particles. We experimentally demonstrate the CM2Net offers 6um lateral, and 24um axial resolution in a 7mm FOV and 800um depth range. We anticipate that this simple and low-cost computational miniature imaging system may be applied to a wide range of large-scale 3D fluorescence imaging and wearable in-vivo neural recordings on mice and other small animals.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

DeepVID: A Self-supervised Deep Learning Framework for Two-photon Voltage Imaging Denoising | Chang Liu Voltage imaging is an evolving tool to continuously image neuronal activities for large number of neurons. Recently, a high-speed low-light two-photon voltage imaging framework was developed, which enabled kilohertz-scanning on population-level neurons in the awake behaving animal. However, with a high […]<\/p>\n","protected":false},"author":12228,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1936"}],"collection":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/users\/12228"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/comments?post=1936"}],"version-history":[{"count":1,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1936\/revisions"}],"predecessor-version":[{"id":1939,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1936\/revisions\/1939"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/media?parent=1936"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/categories?post=1936"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/tags?post=1936"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}