University of Pittsburgh

Georgia Tech Research Corporation

Yu-Ying Liu

Joel Steven Schuman

Hiroshi Ishikawa

James Rehg

Gadi Wollstein

Mei Chen

A method for determining a disease state transition path includes receiving a patient data having functional data and/or structural data related to a patient. Based on the patient data, a first disease state of a plurality of non-overlapping disease states each associated with a predetermined range of functional and/or structural degeneration values may be identified. A second, non-adjacent disease state of the plurality of disease states may be identified based on the patient data. A most probable path between the first disease state and the second disease state may be determined using a two dimensional continuous-time hidden Markov model.

Systems and methods for disease progression modeling

Systems and methods of analyzing an optical coherence tomography image of a retina are discussed. A 2-dimensional slice of the image can be aligned to produce an approximately horizontal image of the retina and an edge map based at least in part on the aligned slice. Also, at least one global representation can be determined based on a (multi-scale) spatial division, such as multi-scale spatial pyramid, on the slice and/or edge map. Creating the local features is based on the specified cell structure of the global representation. The local features can be constructed based on local binary pattern (LBP)-based features. Additionally, a slice can be categorized into one or more categories via one or more classifiers (e.g., support vector machines). Each category can be associated with at least one ocular pathology, and classifying can be based on the constructed global descriptors, which can include the LBP-based local descriptors.

Automated macular pathology diagnosis in three-dimensional (3D) spectral domain optical coherence tomography (SD-OCT) images

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Santa Clara, CA, United States of America

Yu-Ying Liu