TY - GEN
T1 - Segmentation and quantification of cellular load in pulmonary endomicroscopic images using convolutional neural networks
AU - Bonheur, S.
AU - Eldaly, A. K.
AU - Westerfeld, J.
AU - Wilson, D.
AU - Dhaliwal, K.
AU - McLaughlin, S.
AU - Perperidis, A.
N1 - Funding Information:
* This work was supported by EPSRC via EP/K03197X/1.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7/11
Y1 - 2019/7/11
N2 - 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.∗
AB - 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.∗
KW - Cellular load
KW - Convolutional neural networks
KW - Endomicroscopy
KW - Lung
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85067922931&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759412
DO - 10.1109/ISBI.2019.8759412
M3 - Conference contribution
AN - SCOPUS:85067922931
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1018
EP - 1022
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -