Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2022)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
CHF34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
CHF 24.95
Price includes VAT (Switzerland)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
CHF 104.00
Price excludes VAT (Switzerland)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
CHF 130.00
Price excludes VAT (Switzerland)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., Hamed, H.F.A.: A review on brain tumor diagnosis from MRI images: practical implications, key achievements, and lessons learned. Magn. Reson. Imaging 61, 300–318 (2019). https://doi.org/10.1016/j.mri.2019.05.028

    Article  Google Scholar 

  2. Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 16878 (2017). https://doi.org/10.1038/s41598-017-17204-5

    Article  Google Scholar 

  3. de Chaumont, F., et al.: Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9(7), 690–696 (2012). https://doi.org/10.1038/nmeth.2075

  4. Fourier, J.: Mémoire sur la propagation de la chaleur dans les corps solides. Nouveau Bulletin des sciences par la Société philomatique de Paris I, 112–116 (1808)

    Google Scholar 

  5. Jamali, N., Dobson, E.T.A., Eliceiri, K.W., Carpenter, A.E., Cimini, B.A.: 2020 bioimage analysis survey: community experiences and needs for the future. Biol. Imaging 1 (2022). https://doi.org/10.1017/S2633903X21000039. https://www.cambridge.org/core/journals/biological-imaging/article/2020-bioimage-analysis-survey-community-experiences-and-needs-for-the-future/9E824DC0C27568FE5B9D12FB59B1BB90

  6. Kather, J.N., et al.: Large-scale database mining reveals hidden trends and future directions for cancer immunotherapy. Oncoimmunology 7(7), e1444412 (2018). https://doi.org/10.1080/2162402X.2018.1444412

    Article  Google Scholar 

  7. Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019)

    Article  Google Scholar 

  8. Ko, S.Y., et al.: Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck 41(4), 885–891 (2019). https://doi.org/10.1002/hed.25415. https://onlinelibrary.wiley.com/doi/abs/10.1002/hed.25415

  9. Lamprecht, M.R., Sabatini, D.M., Carpenter, A.E.: CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 42(1), 71–75 (2007). https://doi.org/10.2144/000112257

    Article  Google Scholar 

  10. Lee, C.W., Ren, Y.J., Marella, M., Wang, M., Hartke, J., Couto, S.S.: Multiplex immunofluorescence staining and image analysis assay for diffuse large B cell lymphoma. J. Immunol. Methods 478, 112714 (2020). https://doi.org/10.1016/j.jim.2019.112714

    Article  Google Scholar 

  11. Liberati, A., et al.: The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339, b2700 (2009). https://doi.org/10.1136/bmj.b2700

    Article  Google Scholar 

  12. Mahmood, F., et al.: Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39(11), 3257–3267 (2020). https://doi.org/10.1109/TMI.2019.2927182. conference Name: IEEE Transactions on Medical Imaging

  13. Mandelbrot, B.B.: The Fractal Geometry of Nature. Freeman, San Francisco (1983)

    Book  Google Scholar 

  14. Moher, D., et al.: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Control Found. Appl. 4(1), 1 (2015). https://doi.org/10.1186/2046-4053-4-1

  15. Oczeretko, E., Juczewska, M., Kasacka, I.: Fractal geometric analysis of lung cancer angiogenic patterns. Folia Histochem. Cytobiol. 39(Suppl 2), 75–76 (2001)

    Google Scholar 

  16. Partin, A.W., Schoeniger, J.S., Mohler, J.L., Coffey, D.S.: Fourier analysis of cell motility: correlation of motility with metastatic potential. Proc. Natl. Acad. Sci. 86(4), 1254–1258 (1989). https://doi.org/10.1073/pnas.86.4.1254

    Article  Google Scholar 

  17. Peters, M.D.J., Godfrey, C.M., Khalil, H., McInerney, P., Parker, D., Soares, C.B.: Guidance for conducting systematic scoping reviews. JBI Evidence Implement. 13(3), 141–146 (2015). https://doi.org/10.1097/XEB.0000000000000050

    Article  Google Scholar 

  18. Reyes-Aldasoro, C.C., Williams, L.J., Akerman, S., Kanthou, C., Tozer, G.M.: An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections. J. Microsc. 242(3), 262–278 (2011). https://doi.org/10.1111/j.1365-2818.2010.03464.x

    Article  Google Scholar 

  19. Reyes-Aldasoro, C.C.: The proportion of cancer-related entries in PubMed has increased considerably; is cancer truly “The Emperor of All Maladies”? PLoS One 12(3), e0173671 (2017). https://doi.org/10.1371/journal.pone.0173671

  20. Schindelin, J., et al.: Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676–682 (2012). https://doi.org/10.1038/nmeth.2019. https://www.nature.com/articles/nmeth.2019, number: 7 Publisher: Nature Publishing Group

  21. Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675 (2012). https://doi.org/10.1038/nmeth.2089

    Article  Google Scholar 

  22. Serra, J.: Introduction to mathematical morphology. Comput. Vis. Graph. Image Process. 35(3), 283–305 (1986). https://doi.org/10.1016/0734-189X(86)90002-2

    Article  Google Scholar 

  23. Tang, J.H., Yan, F.H., Zhou, M.L., Xu, P.J., Zhou, J., Fan, J.: Evaluation of computer-assisted quantitative volumetric analysis for pre-operative resectability assessment of huge hepatocellular carcinoma. Asian Pac. J. Cancer Prev. APJCP 14(5), 3045–3050 (2013). https://doi.org/10.7314/apjcp.2013.14.5.3045

    Article  Google Scholar 

  24. Theodosiou, T., Vizirianakis, I.S., Angelis, L., Tsaftaris, A., Darzentas, N.: MeSHy: mining unanticipated PubMed information using frequencies of occurrences and concurrences of MeSH terms. J. Biomed. Inform. 44(6), 919–926 (2011). https://doi.org/10.1016/j.jbi.2011.05.009

    Article  Google Scholar 

  25. Tomita, N., Abdollahi, B., Wei, J., Ren, B., Suriawinata, A., Hassanpour, S.: Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw. Open 2(11), e1914645 (2019). https://doi.org/10.1001/jamanetworkopen.2019.14645

  26. Tricco, A.C., et al.: A scoping review of rapid review methods. BMC Med. 13(1), 224 (2015). https://doi.org/10.1186/s12916-015-0465-6

  27. Xie, Y., Zhang, J., Xia, Y.: Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT. Med. Image Anal. 57, 237–248 (2019)

    Article  Google Scholar 

  28. Yung, A., Kay, J., Beale, P., Gibson, K.A., Shaw, T.: Computer-based decision tools for shared therapeutic decision-making in oncology: systematic review. JMIR Cancer 7(4), e31616 (2021). https://doi.org/10.2196/31616

    Article  Google Scholar 

Download references

Acknowlegements

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

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Constantino Carlos Reyes-Aldasoro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youssef Arafat .

Editor information

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

Ethics declarations

The authors declare no conflicts of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12052-7

  • Online ISBN: 978-3-031-12053-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation

  NODES
COMMUNITY 2
Note 2