JOEL S SCHUMAN, MD
Medical Practice in Pittsburgh, PA

2012018, Jul 19, 2012

Nov 11, 2011

13/294601

Hiroshi Ishikawa - Allison Park PA, US
Gadi Wollstein - Pittsburgh PA, US
Joel S. Schuman - Pittsburgh PA, US
Yu-Ying Liu - Atlanta GA, US
James M. Rehg - Atlanta GA, US
Mei Chen - Pittsburgh PA, US

University of Pittsburgh - Of the Commonwealth System of Higher Education - Pittsburgh PA

A61B 3/14, G06K 9/00

600425, 351206, 382131

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.

2012021, Aug 23, 2012

May 27, 2010

13/321301

Juan Xu - Monroeville PA, US
David Tolliver - Pittsburgh PA, US
Hiroshi Ishikawa - Pittsburgh PA, US
Chaim Gad Wollstein - Pittsburgh PA, US
Joel S. Schuman - Pittsburgh PA, US

University of Pittsburgh -- Of the Commonwealth System of Higher Education - Pittsburgh PA

G06K 9/62, G06K 9/00

382131

In the context of the early detection and monitoring of eye diseases, such as glaucoma and diabetic retinopathy, the use of optical coherence tomography presents the difficulty, with respect to blood vessel segmentation, of weak visibility of vessel pattern in the OCT fundus image. To address this problem, a boosting learning approach uses three-dimensional (3D) information to effect automated segmentation of retinal blood vessels. The automated blood vessel segmentation technique described herein is based on 3D spectral domain OCT and provides accurate vessel pattern for clinical analysis, for retinal image registration, and for early diagnosis and monitoring of the progression of glaucoma and other retinal diseases. The technique employs a machine learning algorithm to identify blood vessel automatically in 3D OCT image, in a manner that does not rely on retinal layer segmentation.

2013007, Mar 28, 2013

Nov 19, 2010

13/510732

Jong S. Kim - Pittsburgh PA, US
Hiroshi Ishikawa - Pittsburgh PA, US
Joel S. Schuman - Pittsburgh PA, US
Gadi Wollstein - Pittsburgh PA, US

A61B 3/10

351206

A scan location matching (SLM) method identifies conventional time domain optical coherence tomography (TD-OCT) circle scan locations within three-dimensional spectral domain OCT scan volumes. A technique uses both the SLM algorithm and a mathematical retinal nerve fiber bundle distribution (RNFBD) model, which is a simplified version of the anatomical retinal axon bundle distribution pattern, to normalize TD-OCT thickness measurements for the retinal nerve fiber layer (RNFL) of an off-centered TD-OCT circle scan to a virtual location, centered on the optic nerve head. The RNFBD model eliminates scan-to-scan RNFL thickness measurement variation caused by manual placement of TD-OCT circle scan.

2011017, Jul 21, 2011

Aug 6, 2009

13/056510

Jong S. Kim - Pittsburgh PA, US
Hiroshi Ishikawa - Pittsburgh PA, US
Joel S. Schuman - Pittsburgh PA, US
Gadi Wollstein - Pittsburgh PA, US

G06K 9/00

382131

Advances in optical coherence tomography (OCT) have prompted a transition from time domain OCT, providing 2D OCT images, to spectral domain OCT, which has a 3D imaging capability. Yet conventional technology offers little toward the goal of inter-device compatibility between extant 2D OCT images and newer 3D OCT images for the same or comparable subjects, as in the context of ongoing monitoring the quantitative status of a patient's eyes. The inventive methodology is particularly useful to identify the scan location of tissue in a 2D OCT image within the 3D volumetric data, thereby allowing clinicians to image a patient via 3D OCT, based on available 2D OCT images, with minimal inter-device variation.

7992999, Aug 9, 2011

Apr 21, 2009

12/427184

Juan Xu - Monroeville PA, US
Hiroshi Ishikawa - Pittsburgh PA, US
Joel Steven Schuman - Pittsburgh PA, US

University of Pittsburgh - Of the Commonwealth System of Higher Education - Pittsburgh PA

A61B 3/14

351206, 351205

A fully automated optic nerve head assessment system, based on spectral domain optical coherence tomography, provides essential disc parameters for clinical analysis, early detection, and monitoring of progression.

2012023, Sep 20, 2012

Jan 30, 2012

13/361908

Yiqin Du - Pittsburgh PA, US
James L. Funderburgh - Pittsburgh PA, US
Joel Steven Schuman - Pittsburgh PA, US

A61K 35/12, A61P 27/02, C12N 5/071

424 937, 435366

Provided herein are isolated populations of multipotent stem cells capable of differentiating into trabecular meshwork (TM) cells, methods of obtaining an isolated population of TM cells, and isolated populations of TM cells obtained therefrom. Compositions, kits, and devices comprising the isolated populations of multipotent stem cells or TM cells are also provided herein. Further provided are methods of using the compositions, kits, and devices for decreasing intraocular pressure in an eye, increasing cell density in a trabecular meshwork of an eye, increasing outflow of aqueous humor from an eye, or treating or preventing a medical condition in a subject.

8184885, May 22, 2012

Jul 24, 2008

12/179481

Hiroshi Ishikawa - Allison Park PA, US
Joel S. Schuman - Pittsburgh PA, US
Gadi Wollstein - Pittsburgh PA, US

University of Pittsburgh - of the Commonwealth System of Higher Education - Pittsburgh PA

G06K 9/00

382131, 382154, 600437

A system. The system includes a computing device configured for communication with an imaging system and with a display device. The computing device includes a contour modeling module. The contour modeling module is configured for superimposing reference anchors on a cross-sectional image generated from 3D image data, for generating a line which connects the reference anchors, for sampling the 3D image data in a variable thickness plane defined by the connecting line, and for generating a contour-modeled C-mode image from the sampled 3D image data.