Ge Precision Healthcare LLC

General Electric Company

Nicolas Gogin

Jerome F Knoplioch

Vincent Jerome Morard

Adele Courot

Faycal El Hanchi El Amrani

Celine Pruvot

Lorraine Jammes

Lucile Nosjean

Baptiste Perrin

Melodie Sperandio

Vincent Morard

Mario Viti

Terry Gallico

Jorge Hernandez Londono

Nicolas Coustaud

Etienne Perot

Luc Caselles

Ronan Quelever

Luc Nicodeme

Caroline Clélia My-Linh Dam Hieu

Bassam Abdallah

Laura Dumont

Systems/techniques that facilitate improved ease of image annotation via curved planar reformation are provided. In various embodiments, a system can access a first image depicting an original version of a tubular anatomical structure. In various aspects, the system can generate a second image, by straightening the first image about a centerline of the tubular anatomical structure, such that the second image depicts a straightened version of the tubular anatomical structure. In various instances, the system can generate a third image, by inserting an annotation into the second image, such that the third image depicts the annotation and the straightened version of the tubular anatomical structure. In various cases, the system can generate a fourth image, by un-straightening the third image about the centerline of the tubular anatomical structure, such that the fourth image depicts both the original version of the tubular anatomical structure and an un-straightened version of the annotation.

Ease of image annotation via curved planar reformation

Various methods and systems are provided for image data segmentation. In one example, a method includes receiving a first segmentation input selecting a first set of pixels of a two-dimensional (2D) projected rendering, the 2D projected rendering generated from a 3D medical image dataset, retropropagating the selected first set of pixels to 3D space based on a mapping between the 2D projected rendering and the 3D medical image dataset to form a 3D segmentation mask, and saving the 3D segmentation mask in memory and/or applying the 3D segmentation mask to the 3D medical image dataset, wherein the 2D projected rendering is an intensity projection rendering.

3D interactive annotation using projected views

Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

Methods and systems for real-time image 3D segmentation regularization

Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes memory to store instructions and a processor. The example processor can detect a lesion from an image of a prostate gland and generate a mapping of the lesion from the image to a sector map, the generating the mapping of the lesion comprising identifying a depth region of the lesion, wherein the depth region indicates a location of the lesion along a depth axis. The processor can also provide the sector map comprising a representation of the lesion within the prostate gland mapped from the image to the sector map.

Multi-modal computer-aided diagnosis systems and methods for prostate cancer

A 3D segmentation editing system accurately updates the segmentations of non-edited images of a 3D scan to reflect segmentation edits applied to other images of the scan using localized interpolation. In one or more embodiments, rather than replacing the entireties of the initial segmentations of non-edited images with newly generated, globally interpolated segmentations, the segmentation editing system applies a distance-based criterion to the interpolation of segmentation edits, such that only portions of the segmentations of the non-edited images that correspond to areas that were manually annotated in the edited images will be modified by the interpolation process, and the initial segmentations will be maintained outside of those edited areas. In this way, the system merges the interpolated segmentation with the initial segmentation for each non-edited image in a manner that mitigates unreliable modifications to the initial segmentations in areas far from the edited areas.

Interactive 3D annotation tool with slice interpolation

Methods and systems are provided for detecting coronary lesions in 3D cardiac computed tomography and angiography (CCTA) images using deep neural networks. In an exemplary embodiment, a method for detecting coronary lesions in 3D CCTA images comprises, acquiring a 3D CCTA image of a coronary tree, mapping the 3D CCTA image to a multi-label segmentation map with a trained deep neural network, generating a plurality of 1D parametric curves for a branch of the coronary tree using the multi-label segmentation map, determining a location of a lesion in the branch of the coronary tree using the plurality of 1D parametric curves, and determining a severity score for the lesion based on the plurality of 1D parametric curves.

Systems and methods for multi-label segmentation of cardiac computed tomography and angiography images using deep neural networks

Systems and methods are provided for utilizing histogram views for improved visualization of three-dimensional (3D) medical images. Imaging data obtained during medical imaging examination of a patient may be processed, with the imaging data corresponding to a particular medical imaging technique. At least one medical image may be generated based on processing of the imaging data. Histogram data may be generated based on the at least one medical image. At least one histogram may be displayed along with the at least one medical image or a projection of the at least one medical image, with the at least one histogram including or being is based on the histogram data, and with the at least one histogram being displayed next to and aligned with the at least one medical image or the projection of the at least one medical image.

Methods and systems for utilizing histogram views for improved visualization of three-dimensional (3D) medical images

Apparatus, systems, and methods to generate an edge aware brush for navigation and segmentation of images via a user interface are disclosed. An example processor is to at least: construct a brush for segmentation of image data; provide an interactive representation of the brush with respect to the image data via a user interface, the interactive representation to be displayed and made available for interaction in each of a plurality of viewports provided for display of views of the image data in the user interface; enable update of the viewports based on manipulation of the representation; facilitate display of a preview of a segmentation of the image data corresponding to a location of the representation; and, when the segmentation is confirmed, facilitate generation of an output based on the segmentation.

Systems and methods to semi-automatically segment a 3D medical image using a real-time edge-aware brush

Various methods and systems are provided for determining and characterizing features of an anatomical structure from a medical image. In one example, a method comprises acquiring a plurality of medical images over time during an exam, registering the segmented anatomical structure between the plurality of medical images, segmenting an anatomical structure in a one of the plurality of medical images after registering the plurality of medical images, creating and characterizing a reference region of interest (ROI) in each of the plurality of medical images, determining characteristics of the anatomical structure by tracking pixel values of the segmented and registered anatomical structure over time, and outputting the determined characteristics on a display device.

Methods and systems for characterizing anatomical features in medical images

Methods and apparatus for recording anonymized volumetric data from medical image visualization software are disclosed. An example apparatus includes an image anonymizer to anonymize a medical image outside of a region of interest based on an extraction parameter by degrading the medical image outside a region corresponding to the region of interest and maintaining a resolution of the medical image inside the region corresponding to the region of interest, the extraction parameter based on at least one of an image capture parameter or an instruction from a remote system; and an archive generator to generate a compressed local archive based on the anonymized medical image.

Methods and apparatus for recording anonymized volumetric data from medical image visualization software

Methods and apparatus for recording anonymized volumetric data from medical image visualization software are disclosed. An example method includes receiving a medical image; anonymizing the medical image outside of a region of interest by degrading the medical image outside a region corresponding to the region of interest and maintaining the resolution of the medical image inside the region corresponding to the region of interest; extracting data from the medical image; and generating an archive based on the anonymized medical image and the data.

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Buc, France

Nicolas Gogin