Ge Precision Healthcare LLC

General Electric Company

Jignesh Dholakia

Kavitha Manickam

Deepthi S

Dheeraj Kulkarni

Raghu Prasad

Sandeep Lakshmipathy

Rakesh Shrikant Shevde

Vignesh Singh

Deepthi Sundaran

Veerasravanthi Mudiyam

Apoorva Agarwal

Maggie Fung

A computer-implemented method for estimating cartilage includes pre-processing, via a processor, a segmented image of region of interests (ROIs) of a subject to separate the ROIs into individual ROI volumes, wherein the ROI volumes comprise at least one cartilage region. The computer-implemented method also includes performing, via the processor, surface separation between a respective subchondral surface and a respective articular surface for each ROI volume to extract a respective separate subchondral surface and a respective separate articular surface for each ROI volume. The computer-implemented method further includes estimating, via the processor, cartilage statistics for the at least one cartilage region utilizing a nearest neighbor algorithm based on the respective separate subchondral surface and the respective separate articular surface for each ROI volume.

System and method for automatically estimating knee cartilage thickness for femoral, tibial, patellar, and meniscal cartilages

Method () and system () for image processing for a medical device is provided. The method () includes acquiring () a plurality of images of a subject using an image acquisition system () of the medical device. The method () further includes identifying and differentiating () a plurality of background pixels and a plurality of foreground pixels in the images using the deep learning module (). The method () further includes suppressing () the identified background pixels using a mask and processing the foreground pixels for subsequent reconstruction and/or visualization tasks.

Systems and methods for background aware reconstruction using deep learning

Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, determining at least one spine label seed point on a non-functional image volume, automatically labeling the non-functional image volume with a plurality of spine labels based on the at least one spine label seed point, automatically correcting the geometric misalignments and registering the functional image volume, adjusting the plurality of spine labels and propagating the adjusted spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Systems and methods for whole-body spine labeling

An automatic field-of-view (FOV) prescription system is provided. The system includes an FOV prescription computing device that includes at least one processor electrically coupled to at least one memory device. The at least one processor is programmed to receive localizer images that depict an anatomy, and generate masks associated with the localizer images, wherein the masks represent part of the localizer images that depict the anatomy. The at least one processor is also programmed to calculate bounding boxes surrounding the anatomy based on the masks, generate an FOV based on the bounding boxes, and output the FOV.

Systems and methods of automatic field-of-view (FOV) prescription

Methods and systems are provided for reducing background noise in magnetic resonance (MR) image and parametric map visualization using segmentation and intensity thresholding. An example method includes segmenting the MR image or parametric map into foreground which includes a region of anatomy of interest and background which is outside of the region of anatomy of interest, applying an intensity threshold to the background and not applying the intensity threshold to the foreground of the MR image or parametric map to produce a noise reduced MR image or noise reduced parametric map, and displaying the noise reduced MR image or noise reduced parametric map via a display device.

Systems and methods for background noise reduction in magnetic resonance images

Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, automatically labeling the non-functional image volume with spine labels, automatically correcting the geometric misalignments and registering the functional image volume, and propagating the spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Systems and methods for deep learning based automated spine registration and label propagation

A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.

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Bengaluru, India

Jignesh Dholakia