University of Louisville Research Foundation

Other

University of Massachusetts

Harpal Sandhu

Ahmed Soliman

Ayman Sabry El-Baz

Robert S Keynton

Ahmed Shalaby

Ahmed Sharafeldeen

Mohamed Elsharkawy

Ali Mahmoud

Guruprasad Anapathur Giridharan

Shlomit Schaal

Mohammed Elmogy

Ahmed Eltanboly

Ahmed Sleman

Andrew Switala

Nabila Eldawi

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.

Assessment of pulmonary function in coronavirus patients

Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients.

Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

Segmentation of retinal blood vessels in optical coherence tomography angiography images

A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

Atlas for automatic segmentation of retina layers from OCT images

Growing community of inventors

Louisville, KY, United States of America

Harpal Sandhu