The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 02, 2016

Filed:

Oct. 08, 2014
Applicant:

The Trustees of the University of Pennsylvania, Philadelphia, PA (US);

Inventors:

Joseph H. Gorman, III, Lower Gwynedd, PA (US);

Alison M. Pouch, Abington, PA (US);

Robert C. Gorman, Lower Gwynedd, PA (US);

Hongzhi Wang, Boiling Springs, PA (US);

Paul Yushkevich, Wynnewood, PA (US);

Benjamin M Jackson, Wynnewood, PA (US);

Brian B. Avants, Philadelphia, PA (US);

Chandra M. Sehgal, Wayne, PA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0087 (2013.01); G06T 7/0081 (2013.01); G06T 7/0097 (2013.01); G06T 2207/10136 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20128 (2013.01); G06T 2207/30048 (2013.01); G06T 2210/41 (2013.01);
Abstract

A fully automatic method for segmentation of the mitral leaflets in 3D transesophageal echocardiographic (3D TEE) images is provided. The method combines complementary probabilistic segmentation and geometric modeling techniques to generate 3D patient-specific reconstructions of the mitral leaflets and annulus from 3D TEE image data with no user interaction. In the model-based segmentation framework, mitral leaflet geometry is described with 3D continuous medial representation (cm-rep). To capture leaflet geometry in a target 3D TEE image, a pre-defined cm-rep template of the mitral leaflets is deformed such that the negative log of a Bayesian posterior probability is minimized. The likelihood of the objective function is given by a probabilistic segmentation of the mitral leaflets generated by multi-atlas joint label fusion, while the validity constraints and regularization terms imposed by cm-rep act as shape priors that preserve leaflet topology and constrain model fitting.


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