Segmentation of corneal endothelium images using a U-Net-based convolutional neural network

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network Article history: Diagnostic information regarding the health status of the corneal endothelium may be obtained by ana- Received 23 January 2018 lyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, Received in revised form 5 April 2018 the endothelial cells need to be extracted from the image. Up to today, this has been performed manually Accepted 10 April 2018 or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based MSC: convolutional neural network. Particularly, the network is trained to discriminate pixels located at the 68-U10 borders between cells. The edge probability map outputted by the network is next binarized and skele- 68-T10 tonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting 68-T45 of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The Keywords: resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared Corneal endothelial cells edges. The corresponding mean absolute percentage error of cell number is at http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence in Medicine Elsevier

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0933-3657
D.O.I.
10.1016/j.artmed.2018.04.004
Publisher site
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Abstract

Article history: Diagnostic information regarding the health status of the corneal endothelium may be obtained by ana- Received 23 January 2018 lyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, Received in revised form 5 April 2018 the endothelial cells need to be extracted from the image. Up to today, this has been performed manually Accepted 10 April 2018 or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based MSC: convolutional neural network. Particularly, the network is trained to discriminate pixels located at the 68-U10 borders between cells. The edge probability map outputted by the network is next binarized and skele- 68-T10 tonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting 68-T45 of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The Keywords: resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared Corneal endothelial cells edges. The corresponding mean absolute percentage error of cell number is at

Journal

Artificial Intelligence in MedicineElsevier

Published: Jun 1, 2018

References

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