Deep learning-based super-resolution fluorescence microscopy on small datasets

• URL: http://arxiv.org/abs/2103.04989v1
• Date: Sun, 7 Mar 2021 03:17:47 GMT
• Title: Deep learning-based super-resolution fluorescence microscopy on small datasets
• Authors: Varun Mannam, Yide Zhang, Xiaotong Yuan, and Scott Howard
• Abstract summary: Deep learning has shown the potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. We demonstrate a new convolutional neural network-based approach that is successfully trained with small datasets and super-resolution images. This model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.
• Score: 20.349746411933495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organisms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolution, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown the potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to the photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network-based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.

Related papers

• Deep and shallow data science for multi-scale optical neuroscience [8.21292084298669]
  Computational methods are being developed to meet the need of extracting biologically relevant information. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination.
  arXiv  Detail & Related papers  (2024-02-13T21:30:44Z)
• LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network [0.31402652384742363]
  This paper proposes LUCYD, a novel method for the restoration of volumetric microscopy images. Lucyd combines the Richardson-Lucy deconvolution formula and the fusion of deep features obtained by a fully convolutional network. Our experiments indicate that LUCYD can significantly improve resolution, contrast, and overall quality of microscopy images.
  arXiv  Detail & Related papers  (2023-07-16T10:34:23Z)
• ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis [0.0]
  We propose a novel deep learning workflow combining hierarchical vision transformers with style-based generative adversarial network algorithms. Our study shows that this workflow can generate high-resolution images with a high signal-to-noise ratio (39.1 dB) and realistic synthetic images with a Frechet distance similarity score of 14.88. For the first time, we performed few-shot semantic segmentation of different foraminifera chambers on both generated and synthetic images with high accuracy.
  arXiv  Detail & Related papers  (2023-04-09T18:49:38Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• Super-Resolution and Image Re-projection for Iris Recognition [67.42500312968455]
  Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems.
  arXiv  Detail & Related papers  (2022-10-20T09:46:23Z)
• Advancing biological super-resolution microscopy through deep learning: a brief review [5.677138915301383]
  Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. Deep learning has achieved breakthrough performance in image processing and computer vision. We focus on how deep learning advances reconstruction of super-resolution images.
  arXiv  Detail & Related papers  (2021-06-24T14:44:23Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Axial-to-lateral super-resolution for 3D fluorescence microscopy using unsupervised deep learning [19.515134844947717]
  We present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in fluorescence microscopy. Our method greatly reduces the effort to put into practice as the training of a network requires as little as a single 3D image stack. We demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit, but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
  arXiv  Detail & Related papers  (2021-04-19T16:31:12Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Semi-Supervised StyleGAN for Disentanglement Learning [79.01988132442064]
  Current disentanglement methods face several inherent limitations. We design new architectures and loss functions based on StyleGAN for semi-supervised high-resolution disentanglement learning.
  arXiv  Detail & Related papers  (2020-03-06T22:54:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.