Microsoft Technology Licensing, LLC

General Electric Company

University System of Maryland

Khan Mohammad Siddiqui

Antonio Criminisi

Steven J White

Duncan Paul Robertson

Sayan Dev Pathak

Jamie Daniel Joseph Shotton

Prakash Mahesh

Toby Leonard Sharp

Murali Kumaran Kariathungal

Bryan Dove

Dmitry Pavlov

Kenneth Lopez

Michael Hawkins

Chein-I Chang

Christopher Burt

Wei-min Liu

Ender Konukoglu

Bharath Ramakrishna

Eliot Siegel

Prabhu KrishnaMoorthy

Jeffrey James Whipple

Nabile Safdar

A patient information interface system presents an aggregated, graphical view of patient anatomy and history. The system includes a graphical representation of at least a portion of a human anatomy including one or more indicators, aggregated from a plurality of clinical information sources and located at anatomical locations on the representation, that correspond to clinical events that have occurred in connection with a patient. The system also includes an electronic health record timeline of clinical events for the patient. The timeline includes the same one or more indicators that are displayed on the graphical representation corresponding to clinical events that have occurred in connection with a patient. A selection or change of an indicator on one of the graphical representation or the electronic health record timeline triggers a corresponding selection or change of the indicator on the other of the graphical representation or the electronic health record timeline.

Electronic health record timeline and the human figure

Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Image registration

An example for processing patient image data workflows can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Metadata extraction pipeline

Medical image rendering is described. In an embodiment a medical image visualization engine receives results from an organ recognition system which provide estimated organ centers, bounding boxes and organ classification labels for a given medical image. In examples the visualization engine uses the organ recognition system results to select appropriate transfer functions, bounding regions, clipping planes and camera locations in order to optimally view an organ. For example, a rendering engine uses the selections to render a two-dimensional image of medical diagnostic quality with minimal user input. In an embodiment a graphical user interface populates a list of organs detected in a medical image and a clinician is able to select one organ and immediately be presented with the optimal view of that organ. In an example opacity of background regions of the medical image may be adjusted to provide context for organs presented in a foreground region.

Medical image rendering

Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

Automatic organ localization

Certain embodiments present a system for generating a medical teaching file message. An image identifier labels medical images based upon information pertaining to user interaction with the image. The system also provides an image search engine that scans and indexes images within a database based on a set of rules. The image search engine generates a subset of images from a larger group of medical images within a database based upon the rules. The system also includes a supporting data selection engine for accepting a free text search query from an interface. The supporting data selection engine scans medical databases based on the free text search query, and provides the supporting data search results via an interface. A teaching file message is generated either automatically or manually from the subset of images and the supporting data search results for transmission to a teaching file.

Systems and methods for generating a teaching file message

Techniques for automatically detecting meniscus include receiving pixels from a scanning device directed to a knee of a subject. Also received is a region of interest that includes pixels that correspond to at least a portion of a meniscus of the knee. Without human intervention, a meniscus extraction threshold is determined based on pixel intensities in the region of interest. A meniscus object portion of the scan data is further determined without human intervention based on the meniscus extraction threshold and a geometrical constraint. Other techniques for automatically detecting meniscal tears includes receiving a meniscus object portion of scan data from a scanning device and receiving threshold data that indicates a meniscus extraction threshold. Without human intervention, a propensity for meniscal tears is determined based on the threshold data and the meniscus object portion of the scan data.

Detecting meniscal tears in non-invasive scans

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Kirkland, WA, United States of America

Khan Mohammad Siddiqui