Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks

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    21 Downloads (Pure)


    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.

    Original languageEnglish
    Title of host publicationProceeedings of SPIE
    Subtitle of host publicationMedical Imaging 2019 : Image Processing
    EditorsElsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini, Bennett A. Landman
    Number of pages12
    ISBN (Electronic)978-151062545-7
    Publication statusPublished - 2019
    EventMedical Imaging 2019: Image Processing - San Diego, United States
    Duration: 19 Feb 201921 Feb 2019

    Publication series

    ISSN (Print)0277-786X


    ConferenceMedical Imaging 2019: Image Processing
    CountryUnited States
    CitySan Diego


    • biomarkers
    • Dense U-net
    • fully CNN
    • segmentation
    • specular microscopy


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