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Home / Equine Research Bank / Sci Rep / Deep Learning-Based Quantification of Pulmonary Hemosideroph...
Scientific reports2020; 10(1); 9795; doi: 10.1038/s41598-020-65958-2

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

Abstract: Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss' kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.
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Publication Date: 2020-08-03 PubMed ID: 32747665PubMed Central: PMC7398908DOI: 10.1038/s41598-020-65958-2Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study reports the use of deep learning methods to automatically identify and classify cells related to Exercise-induced Pulmonary Hemorrhage in horses, a task typically done manually – a labor-intensive process with notable observer variability. The automated method was found to be quick, accurate, and consistent, demonstrating potential to improve the current diagnostic methodology.

Research Context and Motivation

  • The study focuses on Exercise-induced pulmonary hemorrhage (EIPH), a common issue in athletic horses that negatively influences their performance.
  • The most sensitive method for diagnosing EIPH is the examination of bronchoalveolar lavage fluid (BALF) under a microscope, notably examining macrophages, a type of white blood cell.
  • Macrophage classification is based on the degree of hemosiderin – a byproduct of blood degradation – present within the cells. This manual grading method is tiresome and monotonous.
  • The researchers aimed to improve this process via modern deep learning techniques, comparing the automated method’s performance to that of nine human experts.

Methodology

  • The team tested advanced deep learning techniques, specifically for classifying single cell macrophages.
  • They compared these automated techniques against nine cytology experts to assess their effectiveness.
  • Object detection methods were also evaluated on a distinct dataset of 17 fully annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages (i.e., macrophages containing hemosiderin).

Findings

  • The deep learning approach achieved a concordance (agreement) rate of 0.85, outperforming the average human expert concordance rate of 0.73.
  • Intra-observer variability (differences in classification made by the same expert) ranged from 0.68 to 0.88, indicating significant variability in human observation.
  • The object detection approach used on WSI had a mean average precision of 0.66 and could complete its computations in less than two minutes.

Conclusion

  • The automated object detection pipeline proposed by the researchers has shown potential to serve as an efficient, reliable, and rapid tool for EIPH diagnosis in WSI.
  • By mitigating high inter- and intra-observer variabilities, it may substantially enhance and expedite the current approach to diagnosing EIPH in horses.

Cite This Article

APA
Marzahl C, Aubreville M, Bertram CA, Stayt J, Jasensky AK, Bartenschlager F, Fragoso-Garcia M, Barton AK, Elsemann S, Jabari S, Krauth J, Madhu P, Voigt J, Hill J, Klopfleisch R, Maier A. (2020). Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides. Sci Rep, 10(1), 9795. https://doi.org/10.1038/s41598-020-65958-2

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 10
Issue: 1
Pages: 9795
PII: 9795

Researcher Affiliations

Marzahl, Christian
  • Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. c.marzahl@euroimmun.de.
  • Research and Development, EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany. c.marzahl@euroimmun.de.
Aubreville, Marc
  • Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Bertram, Christof A
  • Institute of Veterinary Pathology, Freie Universität Berlin, Berlin, Germany.
Stayt, Jason
  • VetPath Laboratory Services, Ascot, Western, Australia.
Jasensky, Anne-Katherine
  • Laboklin GmbH und Co. KG, Bad Kissingen, Germany.
Bartenschlager, Florian
  • Institute of Veterinary Pathology, Freie Universität Berlin, Berlin, Germany.
Fragoso-Garcia, Marco
  • Institute of Veterinary Pathology, Freie Universität Berlin, Berlin, Germany.
Barton, Ann K
  • Equine Clinic, Freie Universität Berlin, Berlin, Germany.
Elsemann, Svenja
  • Department of Neurosurgery, Universitätsklinikum Erlangen, Erlangen, Germany.
Jabari, Samir
  • Institute of Neuropathology, Friedrich Alexander University Erlangen-Nürnberg, Erlangen, Germany.
Krauth, Jens
  • Research and Development, EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany.
Madhu, Prathmesh
  • Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Voigt, Jörn
  • Research and Development, EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany.
Hill, Jenny
  • VetPath Laboratory Services, Ascot, Western, Australia.
Klopfleisch, Robert
  • Institute of Veterinary Pathology, Freie Universität Berlin, Berlin, Germany.
Maier, Andreas
  • Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

MeSH Terms

  • Animals
  • Cytological Techniques
  • Deep Learning
  • Hemorrhage / pathology
  • Horses
  • Lung Diseases / pathology
  • Single-Cell Analysis

Conflict of Interest Statement

The authors declare no competing interests.

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