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Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning

Biology

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning

S. Chaudhary, S. Moon, et al.

Discover how Shivesh Chaudhary, Sihoon Moon, and Hang Lu have developed NIDDL, a groundbreaking supervised deep-denoising method that enhances calcium trace quality while maintaining high imaging speed and low laser power. This innovative technique opens doors to faster and extended imaging experiments across various biological contexts.

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Playback language: English
Abstract
Volumetric functional imaging faces trade-offs between calcium trace quality, imaging speed, and laser power. This paper introduces a supervised deep-denoising method (NIDDL) to address these limitations. NIDDL boasts a small memory footprint, fast training and inference (50-70ms), high accuracy, and generalizability, requiring only small, non-temporally-sequential training datasets (~500 image pairs). The method is demonstrated on whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans, enabling faster and longer-term imaging experiments.
Publisher
Nature Communications
Published On
Sep 02, 2022
Authors
Shivesh Chaudhary, Sihoon Moon, Hang Lu
Tags
volumetric functional imaging
deep-denoising
calcium trace quality
imaging speed
neuroimaging
C. elegans
machine learning

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