Artefact removal in ground truth and noise model deficient sub-cellular nanoscopy images using auto-encoder deep learning

Abstract

Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical constraints, supervised dataset cannot be measured. Due to non-linear spatio-temporal mixing of data and valuable statistics of fluctuations from fluorescent molecules which compete with noise statistics, noise or artefact models in nanoscopy images cannot be explicitly learnt. Therefore, such problem poses unprecedented challenges to deep learning. Here, we propose a robust and versatile simulation-supervised training approach of deep learning auto-encoder architectures for the highly challenging nanoscopy images of sub-cellular structures inside biological samples. We show the proof of concept for one nanoscopy method and investigate the scope of generalizability across structures, noise models, and nanoscopy algorithms not included during simulation-supervised training. We also investigate a variety of loss functions and learning models and discuss the limitation of existing performance metrics for nanoscopy images. We generate valuable insights for this highly challenging and unsolved problem in nanoscopy, and set the foundation for application of deep learning problems in nanoscopy for life sciences.

22 pages, 13 figures

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Deep-STORM: super-resolution single-molecule microscopy by deep learning
• Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy
• BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets
• Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy
• Soft thresholding schemes for multiple signal classification algorithm
• Fluorescence fluctuations-based super-resolution microscopy techniques: an experimental comparative study
• Faster super-resolution imaging with auto-correlation two-step deconvolution
• Simulation-supervised deep learning for analysing organelles states and behaviour in living cells