Automated segmentation and guided correction of endothelial cell images

A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising:

• accessing a training set comprising a plurality of optical microscopy images, wherein each optical microscopy image of the training set comprises an associated set of corneal endothelial cells of a patient associated with that optical microscopy image, and wherein each optical microscopy image of the training set is associated with a ground truth segmentation of plurality of corneal endothelial cells of the set of corneal endothelial cells of that optical microscopy image;
• pre-processing each optical microscopy image of the training set to correct for at least one of shading or illumination artifacts;
• and
• based at least on each pre-processed optical microscopy image and the ground truth segmentation for each optical microscopy image, training a deep learning model to segment a plurality of corneal endothelial cells of a set of corneal endothelial cells of an additional pre-processed optical microscopy image.

An apparatus, comprising:

• memory configured to store an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty;
• one or more processors configured to perform operations comprising:
• 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.