Other

Case Western Reserve University

US Government As Represented by the Secretary of the Army

Laboratory Corporation of America Holdings

The University of North Carolina at Charlotte

Concentric Pipe and Tool Rentals, LLC

Cordis Corporation

Warrior Energy Services Corporation

Uh Cleveland Medical Center

David L Wilson

Kevin Gerald Meyer

Jay Krajewski

James H Keithly

Thomas Taggart

Hao Wu

Hiram G Bezerra

Di Wen

Kevin Miner

Chaitanya Kolluru

Michael S Breen

Andrew M Rollins

Luis A Davila

Mark A Johnson

John Joseph Keating

Robert Gilkeson

Brendan L Eck

Charles Mossey

Daniel Crayon

Beth Ann Benetz

Zhao Wang

Debashish Roy

Celine Latulipe

David Prabhu

Vikash Singh

Sybil Huskey

Chris Murray

Yazan Gharaibeh

Feliciano Sabates

Charles VanCampen

Jacob Levi

Soumya Ray

Douglas C Fisher

Naomi Joseph

Christopher Rinaldi

Jonathan H Lass

Chris Flask

Seok-Won Lee

Rachid Fahmi

William J Tolone

Adam D Troy

Guo-Qiang Zhang

Faruk Orge

Barbara L O'Kane

John E Zweig

Tahseen Minhaz

Marc Thomas Calello

Juhwan Lee

Wei-ning Xiang

Andrew Marcellus Stogner

Elizabeth Fitzpatrick

Michael Russel

Raymond F Muzic, Jr

Hong Lu

Richard Helms

Donglai Huo

Olivier Salvado

Gerald M Saidel

Scott Machuta

Gary L Page

Richard Tortorici

Kyle Joseph Thieman

Yingnan Song

Remo Sebastian Wolfgang Mueller

Feliciano Sergio Sabates, Iii

Jacek Szymanski

Harry J Menegay

Marco Costa

Glenn Friar

Malcolm K Dale

Thomas O'Brien

Ronny Y Shalev

Elwood Wray Johnson

Anthony Scalise

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

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 segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Plaque segmentation in intravascular optical coherence tomography (OCT) images using deep learning

Disclosed are high-throughput vessel sorting systems and methods of sorting sample vessels, such as samples stored in test tubes. A system for sorting a plurality of individual vessels that each contains a sample is disclosed.

Sample sorting system and methods of sorting samples

Disclosed are high-throughput vessel supply systems and methods of supplying sample vessels, such as samples stored in test tubes. A system for supplying a plurality of individual vessels that each contains a sample is disclosed.

Sample supply system and methods of supplying samples

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 employing a pretrained model to determine risk(s) of adverse event(s) based on Computed Tomography (CT) image volume(s) of an artery and/or training a model to determine such risk(s). Example embodiments can determine risk based on territory-specific calcium score(s) and/or intensity/morphological/location features discussed herein. Various embodiments can determine risk(s) based on a single CT image volume and/or change(s) in CT image volumes taken over a series of time points.

Assessment of arterial calcifications

Disclosed are high-throughput vessel receiving systems and methods of receiving sample vessels, such as samples stored in test tubes. A system for receiving a plurality of individual vessels that each contains a sample, and systems and apparatus for guiding, reorienting, collecting, and transporting a plurality of articles, including vessels, are disclosed.

Sample receiving system and methods

Methods and apparatus for grading coronary calcium, including an example apparatus that includes a set of logics and a memory that stores a set of radiographic images. The set of logics includes an image acquisition logic that acquires a set of radiographic images of a region of tissue, the set of radiographic images including a low-energy reference x-ray image and a higher-energy floating x-ray image, a registration logic that produces a set of registered images from a plurality of members of the set of radiographic images, an enhancement logic that produces a set of calcium-enhanced images from the set of registered images, a post-enhancement logic that produces a bone image from the set of calcium-enhanced images, and a grading logic that computes a coronary heart disease prediction score based, at least in part, on the bone image and a set of features extracted from the bone image.

Dual energy x-ray coronary calcium grading

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.

A remote device includes or is configured as an automatic remote control system or device that detects at the remote device in an environment a sound level of audio output from a device. A desired sound level is identified from the detected sound level. The remote device dynamically detects in the environment a current sound level of the audio output. The detecting of the current sound level occurs subsequent to the identifying the desired sound level. The remote device compares the current sound level to the desired sound level, and generates a volume control signal in response to a result of the comparing. The remote device automatically controls, using the volume control signal, a volume of the device to continually maintain at the desired sound level the current sound level detected in the environment.

Remote device configured as automatic controller for audio device

Growing community of inventors

Cleveland Heights, OH, United States of America

David L Wilson