Case Western Reserve University

Uh Cleveland Medical Center

Hao Wu

David L Wilson

Chaitanya Kolluru

Hiram G Bezerra

Rachid Fahmi

Brendan L Eck

Faruk Orge

David Prabhu

Beth Ann Benetz

Jonathan H Lass

Naomi Joseph

Jacob Levi

Yazan Gharaibeh

Tahseen Minhaz

Juhwan Lee

Richard Helms

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

A first set of embodiments relates to an apparatus comprising one or more processors configured to: access three-dimensional (3D) ultrasound imaging of an eye; generate at least one segmented ocular structure by segmenting at least one ocular structure represented in the 3D ultrasound imaging using at least one deep learning ocular structure segmentation model configured to generate a predicted segmentation volume of the at least one ocular structure based on at least one portion of the 3D ultrasound imaging; compute at least one clinical metric associated with the at least one segmented ocular structure based on the at least one segmented ocular structure; and display at least one of: the at least one segmented ocular structure, the at least one clinical metric, the 3D ultrasound imaging, or at least one portion of the 3D ultrasound imaging.

Processing three-dimensional (3D) ultrasound images

Embodiments discussed herein facilitate system-independent quantitative perfusion measurements. One example embodiment is a method, comprising: accessing 4D (Four Dimensional) perfusion imaging data of a tissue, where the 4D perfusion imaging data comprises a plurality of 3D (Three Dimensional) stacks of perfusion imaging data over time; performing at least one of artifact reduction or post-processing on the 4D perfusion image data to generate processed 4D perfusion image data; computing one or more quantitative perfusion parameters for the tissue based at least in part on the processed 4D perfusion image data; and outputting a visual representation of the one or more quantitative perfusion parameters.

System-independent quantitative perfusion imaging

Embodiments discussed herein facilitate classification of vascular plaque. One example embodiment can: access vascular imaging data comprising one or more slices, wherein each slice comprises a plurality of A-lines of that slice; for each A-line of the plurality of A-lines of each slice of the one or more slices: extract one or more features for that A-line, wherein the one or more features for that A-line comprise at least one of: one or more features extracted from that A-line, one or more features extracted from the slice comprising that A-line, or one or more features extracted from the vascular imaging data; provide the one or more features for that A-line to at least one classifier; and generate a classification of that A-line via the at least one classifier, wherein the classification of that A-line is one of fibrocalcific, fibrolipidic, or other.

Assessment of arterial calcifications

Growing community of inventors

Cleveland Heights, OH, United States of America

Hao Wu