Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗

Rafat Damseh; Farida Cheriet; Frederic Lesage

doi:10.1109/EMBC.2018.8512285

Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗

Rafat Damseh, Farida Cheriet, Frederic Lesage

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Segmentation of microvessels measured using two-photon microscopy has been studied in the literature with limited success due to uneven intensities associated with optical imaging and shadowing effects. In this work, we address this problem using a customized version of a recently developed fully convolutional neural network, namely, FC-DensNets. To train and validate the network, manual annotations of 8 angiograms from two-photon microscopy was used. Segmentation results are then compared with that of a state-of-the-art scheme that was developed for the same purpose and also based on deep learning. Experimental results show improved performance of used FC-DenseNet in providing accurate and yet end-to-end segmentation of microvessels in two-photon microscopy.

Original language	English
Title of host publication	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	661-665
Number of pages	5
ISBN (Electronic)	9781538636466
DOIs	https://doi.org/10.1109/EMBC.2018.8512285
Publication status	Published - Oct 26 2018
Externally published	Yes
Event	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: Jul 18 2018 → Jul 21 2018

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2018-July
ISSN (Print)	1557-170X

Conference

Conference	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Country/Territory	United States
City	Honolulu
Period	7/18/18 → 7/21/18

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2018.8512285

Cite this

Damseh, R., Cheriet, F., & Lesage, F. (2018). Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 661-665). Article 8512285 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512285

Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗. / Damseh, Rafat; Cheriet, Farida; Lesage, Frederic.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 661-665 8512285 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Damseh, R, Cheriet, F & Lesage, F 2018, Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018., 8512285, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 661-665, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 7/18/18. https://doi.org/10.1109/EMBC.2018.8512285

Damseh R, Cheriet F, Lesage F. Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 661-665. 8512285. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2018.8512285

Damseh, Rafat ; Cheriet, Farida ; Lesage, Frederic. / Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 661-665 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Segmentation of microvessels measured using two-photon microscopy has been studied in the literature with limited success due to uneven intensities associated with optical imaging and shadowing effects. In this work, we address this problem using a customized version of a recently developed fully convolutional neural network, namely, FC-DensNets. To train and validate the network, manual annotations of 8 angiograms from two-photon microscopy was used. Segmentation results are then compared with that of a state-of-the-art scheme that was developed for the same purpose and also based on deep learning. Experimental results show improved performance of used FC-DenseNet in providing accurate and yet end-to-end segmentation of microvessels in two-photon microscopy.",

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Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy∗

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