Terarecon, Inc.

Other

Terrarecon, Inc.

Tiecheng T Zhao

Robert James Taylor

Jeffrey L Sorenson

Gael Kuhn

David W MacCutcheon

Akio Iwase

Motoaki Saito

Vikram Simha

Junnan Wu

Gang Li

David J G Guigonis

Ryan James Moffitt

Keiji Ito

Chunguang Jia

Tim Frandsen

Nobuyuki Kihara

Misha H Herscu

Jacob Ian Taylor

Steven Rothenberg

Kazuo Takahashi

Hanspeter Pfister

Hugh C Lauer

James M Knittel

Jan C Hardenbergh

Vishal C Bhatia

Shiying Hu

An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

System and method for medical image interpretation

A plurality of image processing engines are hosted within an image processing system. Each image processing engine performs one or more image processing operations or clinical content processing operations on medical images and clinical content. A user interface allows a user to configure the plurality of image processing engines for a particular study of images. The user interface allows the user to configure the plurality of image processing engines in any one of the following configurations: a series configuration where the image processing engines operate in series so that an output from one image processing engine serves as input to a next image processing engine; a parallel configuration where each image processing engine in the plurality of image processing engines operates without input from any other image processing engine in the plurality of image processing engines; or a a hybrid configuration where a first subset of image processing engines operate in a series configuration, and a second subset of image processing engines operate in a parallel configuration.

Medical image identification and interpretation

An automatic medical image processing system includes a series of operation stages, each automating specifying the image processing parameters for processing medical images. In response to an image processing indicator, a first medical image is automatically identified, including determining a first image operation and image processing parameters, without user intervention. The first image operation is performed on the first medical image based on the image processing parameters. A second medical image is generated and transmitted to the client device to be presented therein. The client device displays a message prompting the user whether the user is satisfied with the second medical image. In response to a user input from the client device indicating that the user is unsatisfied with the second medical image, one or more remedial options are presented to allow the user selecting a remedial action to reprocess the first medical image.

Advanced medical image processing wizard

A medical image viewing system allows multiple person viewing of rendered medical images, the medical image viewing system. A medical image processing system receives medical image data and manipulates the medical image data to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first client system receives the first rendered medical image data from the medical image processing system. The first client system displays to a first end user on the first client system, a first 3D medical image that is based on the first rendered medical image data. The first 3D medical image is displayed within an augmented reality environment or a virtual reality environment so that within the augmented reality environment or the virtual reality environment the first end user has a first viewer location and a first viewing angle of the first 3D medical image.

Medical image viewing system

A display device displays a first medical image to a user. Touchless sensor technology detects user actions. The touchless sensor technology includes at least one sensor that does not rely on physical touch to detect the user actions. An image processor that compares detected user actions with currently valid user action within a list of currently valid user actions for the first medical image to determine when a detected user action matches a currently valid user action from the currently valid user actions contained in the list. A medical image processing engine uses metadata identifying the first medical image and an action identifier for a currently valid user action that is matched by the detected user action to generate a second medical image based on the metadata and the action identifier. Thee display device displays the second medical image to the user.

Touchless advanced image processing and visualization

Automated three-dimensional (3D) printing work flow through a network is provided. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer. The 3D segmented medical image data is converted to a converted data file in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information. Identifying information, including patient information, is removed from metadata of the 3D segmented medical image data when converting to the converted data file.

System and method for three-dimensional printing, holographic and virtual reality rendering from medical image processing

An artificial intelligence findings system includes an artificial intelligence findings system. An image identification engine receives new image data pertaining to a patient and past reports and image data pertaining to the patient. The image identification engine uses the past image data pertaining to the patient to identify for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A findings engine receives new image data and processes the new image data to generate findings based on the new image data and based on the identified for study images of patient anatomical structures, anatomical anomalies and anatomical features within the new image data. A communication interface provides the findings to a diagnostic review system that presents the findings to a user. An adjustment engine receives notifications of changes to findings made by a user when using the medical image interpretation system.

An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms that take into account stored preferences for a user. A client viewer presents the findings to the user. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information and based on the adjustments made to the findings by the user adjusts the stored preferences for the user.

Multiple person viewing of rendered medical image is achieved by manipulating an image by the server to produce first rendered medical image data that includes the medical image data plus image manipulation results. A first 3-D medical image that is based on the first rendered medical image data is displayed within an augmented reality environment or a virtual reality environment so that the first end user has a first viewer location and a first viewing angle of the first 3-D medical image. A second 3-D medical image that is based on second rendered medical image data is displayed within the augmented reality environment or the virtual reality environment so that the second end user has at least one of a second viewer location and a second viewing angle that is different from at least one of the first viewer location and the first viewing angle of the first 3-D medical image.

A server receives a request from a client device initiated from a user operating the client device. The server determines a user action identifier (ID) based on the request, the user action ID identifying a user physical action that was captured by one or more sensors. One or more image processing commands are determined based on the user action ID in view of a first medical image currently displayed at the client device. An image processing operation is performed based on the first medical image by executing the one or more image processing commands, generating a second medical image. The second medical image is transmitted to the client device to be presented to the user at the client device.

A work flow is provided through a network. A network server receives through the network a request to convert 3D segmented medical image data to information print-ready for printing on a 3D printer or rendered for viewing. Access to converted data is limited to one or more authorized users. The 3D segmented medical image data to a converted data file. The converted data file is in a medical image data file format that allows multiple sub-segmentation of a particular human anatomy or region of interest. The medical image data file format allows for storage of mask segmentation information, color palette, shadowing, transparency and opacity.

According to one embodiment, at least a portion of medical information of a patient is displayed within MRCS executed within a local device, the medical information including medical treatment history of the patient. At least a portion of the displayed medical information of the patient is transmitted to a medical imaging processing server over a network, where the transmitted medical information includes a patient identifier (ID) of the patient. Both the at least a portion of patient medical information and one or more medical images are displayed within the MRCS, where the medical images are associated with the patient and rendered by the medical image processing server. A set of icons representing a set of image processing tools is displayed within the MRCS, which when activated by a user, allow an image to be manipulated by the imaging processing server.

Integration of medical software and advanced image processing

According to one embodiment, workflow templates are maintained, each workflow template including a predefined sequence of workflow stages associated with a particular type of medical diagnosis or process. In response to a request for processing medical image data from a user, a user identifier (ID) that identifies the user is automatically determined based on the request. At least one of the workflow templates that is specifically configured to process medical image data associated with the user is identified based on the user ID. One or more image processing operations defined by the identified workflow template are performed on the medical image data, generating a scene corresponding to an image view representing the medical image data. The scene associated with each of the workflow stages is stored in a persistent storage, where a scene includes metadata used to recreate a corresponding medical image view subsequently.

Workflow template management for medical image data processing

A cloud server receives a request for accessing medical image data from a client device, where the cloud server provides image processing services to users in image processing steps, resulting in image views. User privileges of a user are determined for accessing the medical image data. In response to receiving a command having a selection of an image view from the client device, the cloud server provides the medical image data based on the selected one or more image views. The user interactions of the user with the medical image data via the selected image views are tracked, including tracking how long in time the user takes to complete a particular medical image processing step. An analysis is performed on the tracked user interactions to determine an overall usage trend of the image views.

Growing community of inventors

Concord, MA, United States of America

Tiecheng T Zhao