Segmentation and quantification of cellular load in pulmonary endomicroscopic images using convolutional neural networks

S. Bonheur, A. K. Eldaly, J. Westerfeld, D. Wilson, K. Dhaliwal, S. McLaughlin, A. Perperidis

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

Abstract / Description of output

Fibre Bundle Endomicroscopy (FBEμ) is an emerging tool that facilitates the real-time structural and functional (via fluorescent dyes) imaging of the distal lung, providing valuable in vivo, in situ indicators across a range pathological or physiological processes. This paper proposes a novel approach for localising and quantifying abnormalities in distal lung, such as increased cellular load, through semantic image segmentation. Two Convolutional Neural Network (CNN) architectures have been tested, (i) U-Net, a purpose specific network for biomedical image applications, and (ii) ENet, a network optimised for fast inference. The results indicate that semantic segmentation of cells as well as quantification of cellular load is viable, with U-Net consistently outperforming ENet, obtaining a pixel accuracy of 0.842 and a correlation (r) with the corresponding manual cellular load estimation of 0. 866.∗

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1018-1022
Number of pages5
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 11 Jul 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period8/04/1911/04/19

Keywords / Materials (for Non-textual outputs)

  • Cellular load
  • Convolutional neural networks
  • Endomicroscopy
  • Lung
  • Semantic segmentation

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