Koninklijke Philips Corporation N.V.

The Penn State Research Foundation

Bank of America Corporation

Kongkuo Lu

Yuechen Qian

Daniel Bystrov

Axel Saalbach

Rafael Wiemker

Eric Cohen-Solal

Christopher Stephen Hall

Jason David Gibbs

William E Higgins

Kun-Chang Yu

Michael W Graham

Shyam Bharat

Tobias Klinder

Thomas Buelow

Alexandra Groth

William Tao Shi

Martin Bergtholdt

Jingping Xu

Gabriel Ryan Mankovich

Sheng Xu

Ranjith Naveen Tellis

Pingkun Yan

Ran Cohen

Ron Papka

Bela Fadida

Lior Wolloch

Jeremy A Geiman

An apparatus includes a display device that displays an input document in a user interface and at least one processor configured to receive a command to determine a document type of the input document and classify the input document to assign at least one document type and a respective confidence score. The processor assigns a significance score to each word of the input document that is indicative of a degree of influence the word has in deciding that the input document is of the at least one document type. The processor determines a level of visual emphasis to be placed on each word of the input document based on the significance score of the word and displays the input document on the display device with each word of the input document visually emphasized in accordance with the determined level of visual emphasis of the word.

Classification of documents

A system () for detecting and labeling structures of interest includes a current patient study database () containing a current patient study () with clinical contextual information (), a statistical model patient report database () containing at least one or more prior patient documents containing clinical contextual information (), an image metadata processing engine () configured to extract metadata for preparing an input for an anatomical structure classifier (), a natural language processing engine () configured to extract clinical context information () from the prior patient documents, an anatomical structure detection and labeling engine () or processor (), and a display device () configured to display findings from the current patient study. The anatomical structure detection and labeling engine () or processor () is configured to identify and label one or more structures of interest () from the extracted metadata and clinical context information () and aggregate series level data.

Optimized anatomical structure of interest labelling

A planning tool, system and method include a processor () and memory () coupled to the processor which stores a planning module (). A user interface () is coupled to the processor and configured to permit a user to select a path through a pathway system (). The planning module is configured to upload one or more slices of an image volume () corresponding to a user-controlled cursor point () guided using the user interface such that as the path is navigated the one or more slices are updated in accordance with a depth of the cursor point in the path.

Automatic depth scrolling and orientation adjustment for semi-automated path planning

A system and method selects a user interface. The method is performed by an imaging device including a gaze tracker. The method includes receiving captured data used to generate an image that is displayed where the image includes identified areas. The method includes tracking a first viewing location on the image by a user of the imaging device. The method includes determining one of the identified areas in the image being viewed based upon the first viewing location. The method includes determining a first user interface to be provided based upon a first correlation to the determined identified area. The method includes displaying the first user interface for use by the user.

Automatic radiology reading session detection

The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.

Self-aware image segmentation methods and systems

A method, system, and program product are provided for user-steered, on-the fly path planning in an image-guided endoscopic procedure, comprising: presenting, on a display, a 2D sectional image showing a region of interest from a preoperative CT scan; defining a control point on the 2D sectional image within a patient's body lumen responsive to a first user input; centering the control point; adjusting a viewing angle about the control point to show a longitudinal section of the body lumen responsive to a second user input; identifying a second point on a planned path within the body lumen responsive to a third user input; extending a planned path connecting the control point and the second point; redefining the second point as a new control point; and repeating the presenting adjusting, identifying, extending, and the redefining steps until the planned path reaches a procedure starting point within the patient's body.

User-steered on-the-fly path planning

In a gaze-tracking system, localized error correction may be applied. The display area may be partitioned into error-correction zones, and each zone may have its own error correcting parameters or function. Calibration points may be defined for each of these zones, and the correction parameters or function within each zone may be dependent upon the observed errors as the user views each calibration point associated with the zone. In like manner, the function applied to smooth the determined gaze point to reduce noise may also be localized by basing its characteristics on the noise exhibited at each calibration point associated with defined noise-calibration zones. The error correcting function and/or the noise smoothing function may be selected based on the requirements of the particular application that receives the corrected and smoothed gaze location.

Gaze tracking system with calibration improvement, accuracy compensation, and gaze localization smoothing

This invention relates generally to medical imaging and, in particular, to a method and system for automatic lymph node station mapping, automatic path or route report generation. A computer-based system for automatically locating the central chest lymph-node stations in a 3D MDCT image is described. Automated analysis methods extract the airway tree, airway-tree centerlines, aorta, pulmonary artery, lungs, key skeletal structures, and major-airway labels. Geometrical and anatomical cues arising from the extracted structures are used to localize the major nodal stations. The system calculates and displays the nodal stations in 3D. Visualization tools within the system enable the user to interact with the stations to locate visible lymph nodes.

Medical image reporting system and method

The present invention relates to a device, system and method for quality assessment of medical images. The device comprises an image input configured to obtain a medical image acquired according to an imaging guideline, a database access unit configured to access a database storing reference images for a plurality of imaging guidelines and for obtaining a reference image based on the imaging guideline used for acquisition of the obtained medical image, an analysis unit configured to analyze the obtained medical image in view of the obtained reference image and/or the used imaging guideline to generate quality information representing the quality of the obtained medical image, and a quality output configured to output the generated quality information.

Device, system and method for quality assessment of medical images

A therapy system and method treats an internal target of a patient (). A treatment plan () is received to treat the internal target. The treatment plan () includes a plurality of treatment fractions including correspondences () between the internal target and an external body surface based on a pre-procedural planning image () and pre-procedural tracking data (). Before selected treatment fractions of the plurality of treatment fractions, a pre-fraction planning image () of the target is received, tracking data () of the external body surface of the patient () is received, and the correspondences () between the internal target and the external body surface are updated based on the received pre-fraction planning image () and the received tracking data (). Therapy is delivered to the patient () in accordance with the treatment plan () and using the updated correspondences ().

Method for improved surface tracking-based motion management and dynamic planning in adaptive external beam radiation therapy

The invention relates to a method of calculating a displacement of an object of interest comprising a step of calculating () a displacement model of said object of interest from adjacent images of a set of pre-acquired images of said object of interest, said displacement model reflects the position of said object of interest along the time. The method is characterized in that the method further comprises the following. A step of determining () a first sub-set of images (S) from said set of pre acquired images within one periodical time cycle of said set of pre-acquired images on the basis of the displacement model. A first step of identifying () a second sub-set of images (S) from newly-acquired images, wherein images in said second sub-set of images (S) are consecutive and have the same most similar image in said first sub-set of images (S), wherein a first set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S), and wherein said most similar image has the largest similarity level in said first set of similarity levels. A first step of selecting () a given image in said second sub-set of images (S) as a first reference image (I). A second step of identifying () a third sub-set of images (S) from said newly-acquired images, wherein images in said third sub-set of images (S) are consecutive and have the same most similar image in said first sub-set of images (S), wherein a set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S), and wherein said most similar image has the largest similarity level in said set of similarity levels. A second step of selecting () a given image in said third sub-set of images (S) as a second reference image (I). A step of calculating () the displacement between said second reference image (I) and said first reference image (I). The invention also relates to a corresponding system of displacement calculation.

Method and system for calculating a displacement of an object of interest

A region of interest segmentation system includes a display device, a gaze-tracking device, a gaze point collector, a boundary identifier, and a region identifier. The display device displays an image. The gaze-tracking device, generates gaze points relative to the displayed image. The gaze point collector selects gaze points from the generated gaze points corresponding to a region of interest of the displayed image. the boundary identifier estimates a boundary based on the selected gaze points. The region identifier segments the region of interest based on the estimated boundary.

Gaze-tracking driven region of interest segmentation

This invention relates generally to medical imaging and, in particular, to a method and system for reconstructing a model path through a branched tubular organ. Novel methodologies and systems segment and define accurate endoluminal surfaces in airway trees, including small peripheral bronchi. An automatic algorithm is described that searches the entire lung volume for airway branches and poses airway-tree segmentation as a global graph-theoretic optimization problem. A suite of interactive segmentation tools for cleaning and extending critical areas of the automatically segmented result is disclosed. A model path is reconstructed through the airway tree.

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Briarcliff Manor, NY, United States of America

Kongkuo Lu