Case Western Reserve University

Jonathan H Lass

David L Wilson

Naomi Joseph

Beth Ann Benetz

Hao Wu

Chaitanya Kolluru

Elizabeth Fitzpatrick

Harry J Menegay

Embodiments discussed herein facilitate determining a prognosis for keratoplasty based on segmented endothelial cells. One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; segmenting, based at least in part on a first model, a plurality of corneal endothelial cells of the set of corneal endothelial cells; calculating one or more features based on the segmented plurality of corneal endothelial cells; and generating, via a second model trained based at least on the one or more features, a prognosis associated with the keratoplasty.

Assessment of endothelial cells and corneas at risk from ophthalmological images

Embodiments discussed herein facilitate automated and/or semi-automated segmentation of endothelial cells via a trained deep learning model. One example embodiment is a method, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; pre-processing the optical microscopy image to generate a pre-processed optical microscopy image via correcting for at least one of shading or illumination artifacts in the optical microscopy image; segmenting, based at least in part on a trained deep learning (DL) model, a plurality of corneal endothelial cells of the set of corneal endothelial cells in the pre-processed optical microscopy image; and displaying, via a graphical user interface (GUI), at least the segmented plurality of corneal endothelial cells.

Automated segmentation and guided correction of endothelial cell images

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Cleveland, OH, United States of America

Jonathan H Lass