Convolution neural network for contour extraction of corneal endothelial cells

Saya Katafuchi; Motohide Yoshimura

doi:10.1117/12.2264430

14 May 2017 Convolution neural network for contour extraction of corneal endothelial cells

Saya Katafuchi, Motohide Yoshimura

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380L (2017) https://doi.org/10.1117/12.2264430
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

The corneal endothelial cells exist on the human’s cornea. To extract every cell contour from them is indispensable for the assessment of cell condition. However, it is difficult to distinguish the contour of large cells from the cytoplasm because of their homogeneity of gray scale pattern. In this paper, we construct the CNNs for the precise cell extraction regardless to scale of the cell. We utilize software library Caffe as a Deep Learning framework. We show the effectiveness of CNNs for the contour extraction of corneal endothelial cells.

Citation Download Citation

Saya Katafuchi and Motohide Yoshimura "Convolution neural network for contour extraction of corneal endothelial cells", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380L (14 May 2017); https://doi.org/10.1117/12.2264430

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