Increasing a microscope's effective field of view via overlapped imaging and machine learning

• URL: http://arxiv.org/abs/2110.04921v1
• Date: Sun, 10 Oct 2021 22:52:36 GMT
• Title: Increasing a microscope's effective field of view via overlapped imaging and machine learning
• Authors: Xing Yao, Vinayak Pathak, Haoran Xi, Amey Chaware, Colin Cooke, Kanghyun Kim, Shiqi Xu, Yuting Li, Timothy Dunn, Pavan Chandra Konda, Kevin C. Zhou, Roarke Horstmeyer
• Abstract summary: This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis.
• Score: 4.23935174235373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

Related papers

• Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging [1.8687965482996822]
  Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel.
  arXiv  Detail & Related papers  (2024-11-02T11:21:33Z)
• Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits [0.7968706282619793]
  Multi multiplexed imaging allows for simultaneous visualization of multiple biomarkers in a single section. Data from multiplexed imaging requires sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. PathML is an AI-powered platform that streamlines image analysis, making complex interpretation accessible for clinical and research settings.
  arXiv  Detail & Related papers  (2024-11-01T18:02:41Z)
• Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging [3.7243418909643093]
  We used confocal and non-confocal split detector images to analyze photoreceptors for improved accuracy. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities.
  arXiv  Detail & Related papers  (2024-10-19T17:10:38Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• 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)
• Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images [9.392002197101965]
  We propose a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art regression-based methods. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.
  arXiv  Detail & Related papers  (2020-11-07T04:02:47Z)
• 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)
• Multi-element microscope optimization by a learned sensing network with composite physical layers [3.2435888122704037]
  Digital microscopes are used to capture images for automated interpretation by computer algorithms. In this work, we investigate an approach to jointly optimize multiple microscope settings, together with a classification network. We show that the network's resulting low-resolution microscope images (20X-comparable) offer a machine learning network sufficient contrast to match the classification performance of corresponding high-resolution imagery.
  arXiv  Detail & Related papers  (2020-06-27T16:49:37Z)

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.