Literature DB >> 31714792

ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images.

Karim T Mukaddem1, Edward J Beard1,2, Batuhan Yildirim1, Jacqueline M Cole1,2,3.   

Abstract

The rise of data science is leading to new paradigms in data-driven materials discovery. This carries an essential notion that large data sources containing chemical structure and property information can be mined in a fashion that detects and exploits structure-property relationships, such that chemicals can be predicted to suit a given material application. The success of material predictions is predicated on these large data sources of chemical structure and property information being suited to a target application. Microscopy is commonly used to characterize chemical structure, especially in fields such as nanotechnology where material properties are highly dependent on the size and shape of nanoparticles. Large data sources of nanoparticle information stemming from microscopy images would thus be highly beneficial. Millions of microscopy images exist, but they lie fragmented across the literature, typically presented individually within a paper article and usually in a qualitative fashion therein, even though they harbor a wealth of numeric information. We present the ImageDataExtractor toolkit that autoidentifies and autoextracts microscopy images from scientific documents, whereupon it autonomously analyzes each image to produce quantitative particle size and shape information about its subject material. Each image is quantified by decoding its scale bar information using optical character recognition, with help from super-resolution convolutional neural networks where required. Individual particles are detected and profiled using various thresholding, segmentation, polygon fitting, and edge correction routines. The high-throughput operational capability of ImageDataExtractor means that it can be used to generate large-data sources of particle information for data-driven materials discovery. Evaluation metrics, precision and recall, are greater than 80% for the majority of the image processing steps, and precision is above 80% for all critical steps. The ImageDataExtractor tool is released under the MIT license and is available to download from http://www.imagedataextractor.org.

Mesh:

Year:  2019        PMID: 31714792     DOI: 10.1021/acs.jcim.9b00734

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

1.  Faces of Contemporary CryoEM Information and Modeling.

Authors:  Giulia Palermo; Yuji Sugita; Willy Wriggers; Rommie E Amaro
Journal:  J Chem Inf Model       Date:  2020-05-26       Impact factor: 4.956

2.  Calculating small-angle scattering intensity functions from electron-microscopy images.

Authors:  Batuhan Yildirim; Adam Washington; James Doutch; Jacqueline M Cole
Journal:  RSC Adv       Date:  2022-06-06       Impact factor: 4.036

3.  Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification.

Authors:  Batuhan Yildirim; Jacqueline M Cole
Journal:  J Chem Inf Model       Date:  2021-03-08       Impact factor: 4.956

Review 4.  Opportunities and challenges of text mining in aterials research.

Authors:  Olga Kononova; Tanjin He; Haoyan Huo; Amalie Trewartha; Elsa A Olivetti; Gerbrand Ceder
Journal:  iScience       Date:  2021-02-06
  4 in total

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