July 2020
Volume 61, Issue 9
Free
ARVO Imaging in the Eye Conference Abstract  |   July 2020
Automatic segmentation with guided correction of post-keratoplasty corneal endothelial cell images and predictive feature extraction.
Author Affiliations & Notes
  • Naomi Joseph
    Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
  • Elizabeth Fitzpatrick
    Computer Science, Case Western Reserve University, Cleveland, Ohio, United States
  • Chaitanya Kolluru
    Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
  • Harry Menegay
    Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio, United States
    Cornea Image Analysis Reading Center, University Hospitals Eye Institute, Cleveland, Ohio, United States
  • Stephanie Burke
    Cornea Image Analysis Reading Center, University Hospitals Eye Institute, Cleveland, Ohio, United States
  • Jonathan H Lass
    Cornea Image Analysis Reading Center, University Hospitals Eye Institute, Cleveland, Ohio, United States
    Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • Beth Ann Benetz
    Cornea Image Analysis Reading Center, University Hospitals Eye Institute, Cleveland, Ohio, United States
    Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • David L Wilson
    Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Naomi Joseph, None; Elizabeth Fitzpatrick, None; Chaitanya Kolluru, None; Harry Menegay, None; Stephanie Burke, None; Jonathan Lass, None; Beth Ann Benetz, None; David Wilson, None
  • Footnotes
    Support  R21 EY029498-01, National Eye Institute: EY20797 and EY20798
Investigative Ophthalmology & Visual Science July 2020, Vol.61, PB0048. doi:
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      Naomi Joseph, Elizabeth Fitzpatrick, Chaitanya Kolluru, Harry Menegay, Stephanie Burke, Jonathan H Lass, Beth Ann Benetz, David L Wilson; Automatic segmentation with guided correction of post-keratoplasty corneal endothelial cell images and predictive feature extraction.. Invest. Ophthalmol. Vis. Sci. 2020;61(9):PB0048.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : We developed a deep-learning-based automatic EC segmentation method followed up by a guided correction graphical user interface (GUI) software to generate accurate cell border representation post-keratoplasty. Furthermore, we have extracted new imaging, textural, and cell graph features that potentially predict transplant rejection.

Methods : We used a subset of 130 specular microscopy images from 26 eyes collected from the Cornea Preservation Time Study (CPTS). Trained readers performed traditional corner analysis using the CAS/EB software (HAI Labs Inc., Lexington, MA). We trained a U-Net model to segment ECs prior to a post-processing pipeline producing single pixel-width borders. We developed a guided correction GUI software, allowing users to add or erase cell borders from suspicious cells, which we highlighted based on under- and over-segmentation conditions. After the cell border segmentation are finalized, we extracted common imaging, textural, and cell graph features. These features will help analyze the healthiness of the EC images following various transplants to determine their successfulness.

Results : Our automatic segmentation method was able to delineate the cell borders of 30 held-out test EC images with a Dice coefficient of 0.87 ± 0.17. From the visual analysis study, there were less than 3% of cells within the ground truth region, which required a second review and possible manual editing. Furthermore, over 500 new cells were identified by our automated segmentation, of which only 31% were flagged for manual editing. We designed a guided correction GUI to highlight 167 suspicious cells (under- or over-segmented cells) for editing. From these finalized segmentations we calculated 190 intensity, textural, and cell graph features.

Conclusions : Our automatic segmentation and guided correction GUI software is able to accurately identify the cell borders of EC images post-keratoplasty. Furthermore, the new features we have calculated to evaluate EC health are novel and possibly indicative of future rejection. The graph features analyze the arrangement of cells in the endothelium which may be indicative of future rejection, since as the endothelium deteriorates, cells start to morph, merge, and create disarray.

This is a 2020 Imaging in the Eye Conference abstract.

 

Examples of post_DSAEK EC image undergone manual and automatic segmentation.

Examples of post_DSAEK EC image undergone manual and automatic segmentation.

 

Screenshot of semi-automated segmentation GUI software.

Screenshot of semi-automated segmentation GUI software.

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