Roshni Rustom Bhagalia

8638999, Jan 28, 2014

Apr 16, 2012

13/448015

Ziyue Xu - Iowa City IA, US
Fei Zhao - Schenectady NY, US
Roshni Rustom Bhagalia - Niskayuna NY, US
Bipul Das - Bangalore, IN

General Electric Company - Niskayuna NY

G06K 9/00

382128, 382173, 382181

A computer-implemented method of post-processing medical image data is provided. The method includes receiving tracked image data representative of multiple blood vessels, generating a binary tree structure for the multiple blood vessels based on a parent-child relationship between branches of the multiple blood vessels, generating a likelihood model for determining a validity of the branches of the multiple blood vessels, and generating a likelihood score for each branch based on the respective branch's compatibility with the likelihood model. The method also includes generating a reconstructed tree for the multiple blood vessels. Compatible branches are included in the reconstructed tree, while invalid branches are not included in the reconstructed tree.

20090245611, Oct 1, 2009

Mar 26, 2008

12/056078

Ali Can - Troy NY, US
Michael John Gerdes - Albany NY, US
Musodiq Olatayo Bello - Niskayuna NY, US
Xiaodong Tao - Niskayuna NY, US
Roshni Bhagalia - Ann Arbor MI, US

General Electric Company - Schenectady NY

G06K 9/00

382133

Techniques for removing image autoflourescence from fluorescently stained biological images are provided herein. The techniques utilize non-negative matrix factorization that may constrain mixing coefficients to be non-negative. The probability of convergence to local minima is reduced by using smoothness constraints. The non-negative matrix factorization algorithm provides the advantage of removing both dark current and autofluorescence.

20180374209, Dec 27, 2018

Jun 27, 2017

15/634657

- Schenectady NY, US
Peter Lamb - Niskayuna NY, US
Roshni Rustom Bhagalia - Niskayuna NY, US
Bipul Das - Bangalore, IN

G06T 7/00
G06T 7/11

The present approach relates, in some aspects, to a multi-level and a multi-channel frame work for segmentation using model-based or “shallow” classification (i.e. learning processes such as linear regression, clustering, support vector machines, and so forth) followed by deep learning. This framework starts with a very low resolution version of the multi-channel data and constructs an shallow classifier with simple features to generate a coarser level tissue mask that in turn is used to crop patches from the high-resolution volume. The cropped volume is then processed using the trained convolution network to perform a deep learning based segmentation within the slices.

20180122082, May 3, 2018

Dec 2, 2016

15/367275

- Schenectady NY, US
Roshni Bhagalia - Niskayuna NY, US
Xiaojie Huang - Schenectady NY, US

G06T 7/143
G06N 3/08
G06N 3/04
G06N 7/00
G06T 7/194
G06T 5/00

Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.

20170337676, Nov 23, 2017

May 23, 2016

15/161679

- Schenectady NY, US
Roshni BHAGALIA - Niskayuna NY, US

GENERAL ELECTRIC COMPANY - Schenectady NY

G06T 7/00
G06K 9/62
A61B 6/03
A61B 6/00

A method of creating a diagnostic evaluation for usual interstitial pneumonia is provided, including obtaining a first plurality of series of HRCT lung slices indicating the presence of UIP, obtaining an identification of UIP and non-UIP voxels, extracting textural and localization features from the UIP and non-UIP voxels, selecting features that are more accurate in differentiating UIP voxels from non-UIP voxels than other features are, eliminating features highly correlated with a more accurate feature, and constructing a predictive model by performing a second classifier to provide a probability that a voxel signifies the presence of UIP. Also provided is a method of identifying UIP in a subject's lung by applying a diagnostic evaluation for UIP that was created with the foregoing method.

20140294276, Oct 2, 2014

Mar 28, 2013

13/852781

- Schenectady NY, US
Srikrishnan V - Bangalore, IN
Roshni Rustom Bhagalia - Niskayuna NY, US
Biqul Das - Bangalore, IN

General Electric Company - Schenectady NY

G06T 7/00

382132

A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit.