Ge Precision Healthcare LLC

General Electric Company

Ravi Soni

Gopal Biligeri Avinash

Tao Tan

Min Zhang

Gireesha Chinthamani Rao

Dibyajyoti Pati

Rakesh Mullick

Krishna Seetharam Shriram

Vikram Reddy Melapudi

Katelyn Rose Nye

Pál Tegzes

Bipul Das

German Guillermo Vera Gonzalez

Zili Ma

Zita Herczeg

Máté Fejes

John Michael Sabol

Venkata Ratnam Saripalli

Khaled Salem Younis

Sohan Rashmi Ranjan

Lehel Ferenczi

Utkarsh Agrawal

Junpyo Hong

Poonam Dalal

Dániel Attila Szabó

Barbara Darázs

Jiahui Guan

Yash N Shah

Dejun Wang

Yaan Ge

James Gualtieri

László Ruskó

Teri Lynn Fischer

Beth Ann Heckel

Chen Liu

Justin M Wanek

Justin Wright

Balázs Péter Cziria

Lehel Mihály Ferenczi

Fuqiang Chen

Hans Krupakar

Rajesh Kumar Tamada

Aizhen Zhou

Venkata Ratna Saripalli

Sean P OConnor

Kristin Anderson

Masaki Ikuta

Balázs P Cziria

Computer processing techniques are described for augmenting computed tomography (CT) images with synthetic artifacts for artificial intelligence (AI) applications. According to an example, a computer-implemented method can include generating, by a system comprising a processor, synthetic artifact data corresponding to one or more CT image artifacts, wherein the synthetic artifact data comprises anatomy agnostic synthetic representations of the one or more CT image artifacts. The method further includes generating, by the system, augmented CT images comprising the one or more CT image artifacts using the synthetic artifact data. In one or more examples, the method can further include training, by the system, a medical image inferencing model to perform an inferencing task using the augmented CT images as training images.

Artifact-driven data synthesis in computed tomography

Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Multimodality image processing techniques for training image data generation and usage thereof for developing mono-modality image inferencing models

Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

System and methods for inferring thickness of object classes of interest in two-dimensional medical images using deep neural networks

An artificial intelligence (AI) lead marker detection system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to scan post-exposure X-ray images to detect and insert various lead markers, to digitize information provided by the type and location of the lead marker, and to employ the marker information in different X-ray system workflow automations. The marker information obtained by the AI lead marker detection system can also provide useful data for use in downstream clinical and quality applications apart from the X-ray system, such as either AI or non-AI analytical applications.

X-ray lead marker detection system for x-ray imaging system

A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Artificial intelligence-based dual energy x-ray image motion correction training method and system

An artificial intelligence (AI) X-ray image information detection and correction system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect various types of information or characteristics of the X-ray image, including, but not limited to, anatomy, view, orientation and laterality of the X-ray image, along with an anatomical landmark segmentation. The information detected about the X-ray image can then be stored by the AI system in association with the X-ray image for use in various downstream X-ray system workflow automations and/or reviews of the X-ray image.

System and method for automatic detection and correction of anatomy, view, orientation and laterality in X-ray image for X-ray imaging system

Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Methods and systems for image registration

Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Multi-layer image registration

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

System and methods for visualizing variations in labeled image sequences for development of machine learning models

Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

Medical machine synthetic data and corresponding event generation

System and methods for inferring thickness of anatomical classes of interest in two-dimensional medical images using deep neural networks

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Systems and methods for detecting laterality of a medical image

Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images. In various instances, a deployable annotated training image can be formed by varying at least one geometric characteristic of an intermediate annotated training image. In various embodiments, a training component can train a machine learning model on the set of deployable annotated training images.

Synthetic training data generation for improved machine learning model generalizability

Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like.

Deep neural network based identification of realistic synthetic images generated using a generative adversarial network

Growing community of inventors

San Ramon, CA, United States of America

Ravi Soni