General Electric Company

Ge Medical Systems Global Technology Company, LLC

Matthieu Denis Ferrant

Saad Ahmed Sirohey

Jerome F Knoplioch

Kelly Lynn Karau

Beth Ann Heckel

Gopal Biligeri Avinash

Paul Edgar Licato

Ricardo Scott Avila

Rahul Bhotika

Cheryl Ruth Jones

Paulo Ricardo Dos Santos Mendonca

Kelly Lynn Piacsek

Pascal Allain

Jérôme Francois Knoplioch

Bob L Beckett

Claire Naslot

Patrick Derréal

Guy-Michel Hourdin

Mirelle Audet

An image display system includes user configurable viewports for the review and analysis of image data. The user configurable viewports include a review viewport and an analysis viewport. The review viewport displays in a review mode, a plurality of image views of the image data. The analysis viewport displays in an analysis mode, a plurality of image views of the image data. The review viewport and the analysis viewport may be configured for simultaneous display of related image data.

Clinical review and analysis work flow for lung nodule assessment

Methods and systems for quantification of a selected attribute of an image volume are provided. The system is configured to receive an image dataset for a volume of interest, process the dataset for a selected attribute based at least on one of shape and texture to obtain a plurality of responses, and compute an index of an aggregate of a plurality of obtained responses.

Methods and systems for monitoring tumor burden

A method includes generating a first response including a plurality of potential first objects of interest of a first shape from data regarding a region of interest (ROI), generating a second response including a plurality of second objects of interest for a second shape different than the first shape from the data regarding the ROI, and performing a morphological closing on the second response to produce a second cleaned response including a plurality of completed second objects. The method also includes decreasing the number of potential objects of interest in the first response by deleting any potential first objects that overlap any completed second objects to obtain a cleaned first response, and generating an image using at least one of the first cleaned response and the second cleaned response.

Method and apparatus to facilitate visualization and detection of anatomical shapes using post-processing of 3D shape filtering

Methods and systems for processing image data are described. One method includes identifying image data corresponding to an imaged trachea and identifying image data corresponding to imaged lungs. The method further includes separating the image data corresponding to the imaged trachea from the imaged data corresponding to the imaged lungs.

Method and system for segmentation of computed tomography image data

A method for analyzing a three-dimensional (3D) image representative of an initial volume of interest (VOI) containing a lesion is disclosed. The 3D image has an associated binary map having voxels associated with a thresholded variance map of the initial VOI. The initial VOI has boundaries that may be defined by a cube. A final VOI is determined that is less than the initial VOI, where the final VOI is a function of the number of transitions from a boundary voxel to a filtered voxel at an outer boundary of the lesion. The final VOI is then analyzed to determine a nodule consistency class, classified based upon the computed nodule consistency class by applying a voxel-clustering algorithm, and displayed with respect to three classifications of tissue.

Method and apparatus for analyzing three-dimensional images containing voxels

For the automatic detection and characterization of nodules in a tomographic image of an anatomical zone of a patient, the image is segmented for identifying therein a region of interest, and the segmented image is processed for identifying the nodule. During the processing step, an ellipsoid inscribed in the region of interest is modeled for deciding whether the image elements inscribed in this ellipsoid correspond to a nodule, and, for each zone of the region of interest extending beyond the ellipsoid, the image elements are identified which do not belong to the module according to mathematical morphologic criteria.

Method for the detection and automatic characterization of nodules in a tomographic image and a system of medical imaging by tomodensimetry

A method for detection and classification of nodules (N) in a tomographic image of an anatomical zone of a patient includes segmenting the image to identify therein a region of interest including at least one nodule, and classifying regions of the nodule as one of solid, part-solid, and non-solid.

Methods and apparatus for the classification of nodules

An object detection apparatus includes a data acquisition system configured to acquire diagnostic data of the subject, an image reconstructor configured to reconstruct at least one image of the subject from the diagnostic data, and a data retrieval device configured to retrieve a first set of bookmarks identifying a first set of objects of interest in the at least one image. The object detection apparatus further includes a computer programmed to display the at least one image on a console and detect input from the user corresponding to a second set of bookmarks identifying a second set of objects of interest in the image. The computer is further programmed to selectively allow the user to incorporate each bookmark of the first set of bookmarks into the second set of bookmarks.

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Saint Remy les Chevreuse, France

Matthieu Denis Ferrant