Extracting and classifying salient fields of view from microscopy slides of tuberculosis bacteria

Marios Zachariou*, Oggie Arandelovic, Evelin Dombay, Wilber Sabiiti, Bariki Anyamkisye Mtafya, Derek James Sloan

*Corresponding author for this work

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

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Tuberculosis is one of the most serious infectious diseases, and its treatment is highly dependent on early detection. Microscopy-based analysis of sputum images for bacilli identification is a common technique used for both diagnosis and treatment monitoring. However, it [is] a challenging process since sputum analysis requires time and highly trained experts to avoid potentially fatal mistakes. Capturing fields of view (FOVs) from high resolution whole slide images is a laborious procedure, since they are manually localized and then examined to determine the presence of bacteria. In the present paper we propose a method that automates the process, thus greatly reducing the amount of human labour. In particular, we (i) describe an image processing based method for the extraction of a FOV representation which emphasises salient, bacterial content, while suppressing confounding visual information, and (ii) introduce a novel deep learning based architecture which learns from coarsely labelled FOV images and the corresponding binary masks, and then classifies novel FOV images as salient (bacteria containing) or not. Using a real-world data corpus, the proposed method is shown to out-perform 12 state of the art methods in the literature, achieving (i) an approximately 10% lower overall error rate than the next best model and (ii) perfect sensitivity (7% higher than the next best model).
Original languageEnglish
Title of host publicationPattern recognition and artificial intelligence
Subtitle of host publicationthird international conference, ICPRAI 2022, Paris, France, June 1–3, 2022, proceedings, part I
EditorsMounîm El Yacoubi, Eric Granger, Pong Chi Yuen, Umapada Pal, Nicole Vincent
Place of PublicationCham
Number of pages12
ISBN (Electronic)9783031090370
ISBN (Print)9783031090363
Publication statusPublished - 2 Jun 2022
Event3rd International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2022) - Virtual and In-Person, Paris, France
Duration: 1 Jun 20223 Jun 2022
Conference number: 3

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2022)
Abbreviated titleICPRAI 2022
Internet address


  • Whole slide images
  • Fluorescence microscopy
  • Image processing
  • Artificial intelligence
  • Medicine
  • Infection
  • Respiratory system


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