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Computational Image Analysis Techniques, Programming Languages and Software Platforms Used in Cancer Research: A Scoping Review

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Medical Image Understanding and Analysis (MIUA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13413))

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Abstract

Background: Cancer-related research, as indicated by the number of entries in Medline, the National Library of Medicine of the USA, has dominated the medical literature. An important component of this research is based on the use of computational techniques to analyse the data produced by the many acquisition modalities. This paper presents a review of the computational image analysis techniques that have been applied to cancer. The review was performed through automated mining of Medline/PubMed entries with a combination of keywords. In addition, the programming languages and software platforms through which these techniques are applied were also reviewed.

Methods: Automatic mining of Medline/PubMed was performed with a series of specific keywords that identified different computational techniques. These keywords focused on traditional image processing and computer vision techniques, machine learning techniques, deep learning techniques, programming languages and software platforms.

Results: The entries related to traditional image processing and computer vision techniques have decreased at the same time that machine learning and deep learning have increased significantly. Within deep learning, the keyword that returned the highest number of entries was convolutional neural network. Within the programming languages and software environments, Fiji and ImageJ were the most popular, followed by Matlab, R, and Python. Within the more specialised softwares, QuPath has had a sharp growth overtaking other platforms like ICY and CellProfiler.

Conclusions: The techniques of artificial intelligence techniques and deep learning have grown to overtake most other image analysis techniques and the trend at which they grow is still rising. The most used technique has been convolutional neural networks, commonly used to analyse and classify images. All the code related to this work is available through GitHub: https://github.com/youssefarafat/Scoping-Review.

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Acknowlegements

We acknowledge Dr Robert Noble for the useful discussions regarding this work.

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Authors and Affiliations

  1. GiCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London, UK

    Youssef Arafat & Constantino Carlos Reyes-Aldasoro

Authors
  1. Youssef Arafat
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  2. Constantino Carlos Reyes-Aldasoro
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Correspondence to Youssef Arafat .

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Editors and Affiliations

  1. Imperial College London, London, UK

    Guang Yang

  2. University of Cambridge, Cambridge, UK

    Angelica Aviles-Rivero

  3. University of Cambridge, Cambridge, UK

    Michael Roberts

  4. University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

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Arafat, Y., Reyes-Aldasoro, C.C. (2022). Computational Image Analysis Techniques, Programming Languages and Software Platforms Used in Cancer Research: A Scoping Review. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_61

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  • DOI: https://doi.org/10.1007/978-3-031-12053-4_61

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