General Electric Company

Ge Lighting Solutions LLC

Michael J MacDonald

Gary Robert Allen

Jian Li

Thomas Kwok-Fah Foo

David Martin Mills

Zhiyong Wang

David C Dudik

Alberto Santamaria-Pang

Patrick Joseph Howard

Soumya Ghose

Desmond Teck Beng Yeo

Yousef Ahmed Al-Kofahi

Jhimli Mitra

Bruce Courtney Amm

Gustino J Lanese

Jinlin Cao

Shourya Sarcar

Aritra Chowdhury

Peter Arjan Wassenaar

Yanqing Xie

Eric Seth Moderbacher

The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.

Biopsy workflow using multimodal imaging

An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.

Systems and methods for automatic defect recognition

LED lamp with an elevated light unit

A light emitting apparatus comprises: an LED-based light source; a spherical, spheroidal, ovoid, egg-shaped, or toroidal diffuser generating a Lambertian light intensity distribution output at any point on the diffuser surface responsive to illumination inside the diffuser; and a base including a base connector. The LED based light source, the diffuser, and the base are secured together as a unitary LED lamp installable in a lighting socket by connecting the base connector with the lighting socket. The base is operatively connected with the LED based light source in the unitary LED lamp to electrically power the LED based light source using electrical power received at the base connector.

Light emitting diode (LED) based lamp

Light bulb

Growing community of inventors

Cleveland, OH, United States of America

Michael J MacDonald