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Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks
Author(s): Juan P. Vigueras-Guillén; Hans G. Lemij; Jeroen van Rooij; Koenraad A. Vermeer; Lucas J. van Vliet
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Paper Abstract

In images of the corneal endothelium (CE) acquired by specular microscopy, endothelial cells are commonly only visible in a part of the image due to varying contrast, mainly caused by challenging imaging conditions as a result of a strongly curved endothelium. In order to estimate the morphometric parameters of the corneal endothelium, the analyses need to be restricted to trustworthy regions – the region of interest (ROI) – where individual cells are discernible. We developed an automatic method to find the ROI by Dense U-nets, a densely connected network of convolutional layers. We tested the method on a heterogeneous dataset of 140 images, which contains a large number of blurred, noisy, and/or out of focus images, where the selection of the ROI for automatic biomarker extraction is vital. By using edge images as input, which can be estimated after retraining the same network, Dense U-net detected the trustworthy areas with an accuracy of 98.94% and an area under the ROC curve (AUC) of 0.998, without being affected by the class imbalance (9:1 in our dataset). After applying the estimated ROI to the edge images, the mean absolute percentage error (MAPE) in the estimated endothelial parameters was 0.80% for ECD, 3.60% for CV, and 2.55% for HEX.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094931 (15 March 2019); doi: 10.1117/12.2512641
Show Author Affiliations
Juan P. Vigueras-Guillén, Technische Univ. Delft (Netherlands)
Rotterdam Ophthalmic Institute (Netherlands)
Hans G. Lemij, The Rotterdam Eye Hospital (Netherlands)
Jeroen van Rooij, The Rotterdam Eye Hospital (Netherlands)
Koenraad A. Vermeer, Rotterdam Ophthalmic Institute (Netherlands)
Lucas J. van Vliet, Technische Univ. Delft (Netherlands)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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