Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging.

ABSTRACT: Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed Inf

SUBMITTER: Gritti N 

PROVIDER: S-EPMC10864180 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature