Automated TMA-Core-Detection Algorithm

Lilla Elo, Robert Paulik, Gabor Kiszler, Tamas Micsik, Tamas Szekely, Huba Hajdu, Miklos Kozlovszky, Bela Molnar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tissue microarray (TMA) is a high-throughput technology for the analysis of molecular markers in oncology. This method supports the presentation of several different tissue samples-TMA cores-in one singe glass slide. However, because of the large size of TMA cores, the 'identification and analysis' procedure is a more or less time-consuming method. The TMA core-finding algorithm detailed in this study detects each of the TMA cores on the slide and it creates outline annotation around the cores automatically. A validation study is also presented, through which detection accuracy of this algorithm for detecting cores on brightfield and fluorescent slides have been measured. We have found a 77.5% detection accuracy in average, so based on this result we can conclude that our TMA core detection solution can be utilized as a useful tool for supporting TMA analysis.

Original languageEnglish
Title of host publicationIWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages159-166
Number of pages8
ISBN (Electronic)9781728109671
DOIs
Publication statusPublished - Jul 2019
Event2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019 - Budapest, Hungary
Duration: Jul 3 2019Jul 5 2019

Publication series

NameIWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings

Conference

Conference2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019
CountryHungary
CityBudapest
Period7/3/197/5/19

Keywords

  • core detection
  • digital pathology
  • image processing
  • tissue microarray

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Neuroscience (miscellaneous)

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  • Cite this

    Elo, L., Paulik, R., Kiszler, G., Micsik, T., Szekely, T., Hajdu, H., Kozlovszky, M., & Molnar, B. (2019). Automated TMA-Core-Detection Algorithm. In IWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings (pp. 159-166). [9114446] (IWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWOBI47054.2019.9114446