Koninklijke Philips Corporation N.v.

Astrid Ruth Franz

Daniel Bystrov

Rafael Wiemker

Tobias Klinder

Cristian Lorenz

Fabian Wenzel

Nicole Schadewaldt

Carsten Meyer

Thomas Heiko Stehle

Heinrich Schulz

Jochen Keupp

Torbjoern Vik

Stefanie Remmele

Jens Von Berg

Thomas Netsch

Steffen Renisch

Thomas Blaffert

Harald Sepp Heese

Christian Stehning

Sebastian Peter Michael Dries

Ingwer Curt Carlsen

Hans Barschdorf

Dominik Benjamin Kutra

Elizabeth Anne Moore

Robin M B Wolz

Robin Mitja Benjamin Wolz

A system and a method are provided for enabling a user to interactively navigate through a set of slice images, the set of slice images jointly representing an image volume showing an anatomical structure of a patient. A user may be enabled to switch from a static viewing mode to a navigation mode based on navigation commands received from a user input device operable by the user. A display processor may be configured for, in the static viewing mode, generating an output image comprising one slice image of the set of slice images. The display processor may be configured for, in the navigation mode, replacing the said one slice image in the output image by a volume rendering of a slab of the image volume, the slab comprising more than one slice image. The system and method thus selectively switch to volume rendering, namely during navigation, whereas in a static (i.e., non-navigation) viewing mode, a slice image is shown. Advantageously, the user may thus follow structures more accurately when navigate through a volume image, thereby more quickly and accurately identifying slice images of interest.

Medical image navigation system

A symmetric model representing anatomical structures of the brain is adapted to a brain scan image with a transform. First and second points provided on first and second hemispheres of the brain image and a patient-specific symmetric anatomical model of the brain is computed based on the transformation.

Improving symmetry in brain scans

System and related method to visualize image data. The system comprises an input port (IN) for receiving i) image data comprising a range of intensity values converted from signals acquired by an imaging apparatus in respect of an imaged object, and ii) a definition of a first transfer function configured to map a data interval within said range of said intensity values to an image interval of image values. A transition region identifier (TRI) identifies from among intensity values outside said data interval, one or more transition intensity values representative of a transition in composition and/or configuration of said object or of a transition in respect of a physical property in relation to said object. A transfer function generator (TFG) generates for said intensity values outside said data interval a second transfer function. The second transfer function is non-linear and has a respective gradient that is locally maximal around said transition intensity values. A renderer (RD) then renders, on a display unit (MT), a visualization of at least a part of said image data based on the two transfer functions.

Selecting transfer functions for displaying medical images

A medical imaging system () includes a workstation (), a coarse segmenter (), a fine segmenter (), and an enclosed tissue identification module (). The workstation () includes at least one input device () for receiving a selected location as a seed in a first contrasted tissue type and a display device () which displays a diagnostic image delineating a first segmented region of a first tissue type and a second segmented region of a second contrasted tissue type and identified regions which include regions fully enclosed by the first segmented region as a third tissue type. The coarse segmenter () grows a coarse segmented region of coarse voxels for each contrasted tissue type from the seed location based on a first growing algorithm and a growing fraction for each contrasted tissue type. The seed location for growing the second contrasted tissue type includes the first coarse segmented region and any fully enclosed coarse voxels, and each coarse voxel includes an aggregation of voxels and a maximum and a minimum of the voxel intensities. The fine segmenter () grows a segmented region of voxels for each contrasted tissue type from the seed location and bounded by the second coarse segmented region based on a second growing algorithm and a growing fraction for each contrasted tissue type initially set to the growing fraction for the corresponding region. The seed location for growing the second contrasted tissue type includes the first segmented region and any identified regions. The enclosed tissue identification module () identifies any regions of voxels fully enclosed by the first segmented region as being of the third tissue type. The coarse segmenter, the fine segmenter, and the enclosed tissue identification module are implemented by an electronic data processing device.

Tumor segmentation and tissue classification in 3D multi-contrast

A device for segmenting an image of a subject (), includes a data interface for receiving an image of the subject (), which image depicts a structure of said subject (). A translation unit translates a user-initiated motion of an image positioner into a first contour () surrounding said structure. A motion parameter registering unit registers a motion parameter of said user-initiated motion to said first contour (). The motion parameter includes a speed and/or an acceleration of an image positioner. An image control point unit distributes a plurality of image control points () on the first contour with a density decreasing with the motion parameter. A segmentation unit segments the image by determining a second contour () within the first contour based on the plurality of image control points (). The segmentation unit is configured to use one or more segmentation functions.

Device, system and method for segmenting an image of a subject

An apparatus and related method for image viewing. The apparatus (V) allows to store, learn and remember preferred user views &#x3b1;for each anatomical structure F-Fof interest. In any new image, the apparatus (V) affords automatically generating the preferred by the user for one or more of the structures (F-F) by a simple user input operation such as clicking with a mouse (PT) on any position within the displayed structure of interest (F-F).

Preferred view generation on structure level based on user preferences

A method includes obtaining a single training image from a set of training images in a data repository. The method further includes generating an initial tissue class atlas based on the obtained single training image. The initial tissue class atlas includes two or more different tissue class images corresponding to two or more different tissue classes. The method further includes registering the remaining training images of the set of training images to the initial tissue class atlas. The method further includes generating a quality metric for each of the registered images. The method further includes evaluating the quality metric of each of the registered image with a predetermined evaluation criterion. The method further includes identifying a sub-set of images from the set of training images that satisfy the evaluation criterion. The method further includes generating a subsequent tissue class atlas based on the identified sub-set of the set of training images.

Unsupervised training for an atlas-based registration

A system and method directed to receiving a first data set corresponding to patient data at a first time, receiving a second data set corresponding to patient data at a second time, segmenting a first region of interest in the first data set and a second region of interest in the second data set, the first and second regions corresponding to one another and aligning the first region of interest with the second region of interest to highlight a first contour indicating a change in size, shape and orientation between the first and second regions of interest.

Interactive follow-up visualization

The invention relates to a system for segmenting an object in image data using model-based image segmentation. The system comprises a feature unit for identifying features in the image data for computing an external energy of a mesh on the basis of a current position of the mesh. The feature unit further comprises a candidate feature unit for selecting a plurality of candidate features in the image data. The feature unit further comprises a position unit for determining a position of each candidate feature of the plurality of the candidate features relative to a region of the image data. The feature unit further comprises a feature function unit for computing a strength of each candidate feature. The feature unit further comprises an evaluation unit for evaluating each candidate feature of the plurality of candidate features and for identifying the feature among the plurality of candidate features based on this evaluation.

Mesh collision avoidance

The invention relates to a system () for segmenting a plurality of objects in image data using simultaneous model-based image segmentation. A surface mesh is adapted to each object to be segmented. To avoid or reduce the possibility of mesh collision, a plurality of connecting edges for connecting two proximal meshes are used. A connection energy defined for the plurality of connecting edges allows controlling the spatial relationship between the first and second mesh. This is achieved by including in the connection energy expression terms that will increase the connection energy when the lengths of edges of the plurality of connecting edges connecting the first and second mesh decrease. Using the reference configuration of the plurality of connecting edges defined based on the pre-positioned first and second mesh allows taking into account prior knowledge about a typical spatial relationship between the first and second object of the plurality of objects.

Simultaneous model-based segmentation of objects satisfying pre-defined spatial relationships

The present invention relates to an apparatus () for segmenting an object comprising sub-objects shown in an object image. The apparatus comprises a feature image generation unit () for generating a feature image showing features related to intermediate regions between the sub-objects and a segmentation unit () for segmenting the sub-objects by using the object image and the feature image. Preferentially, the feature image generation unit () is adapted for generating a feature image from the object image. In a further embodiment, the feature image generation unit () comprises a feature enhancing unit for enhancing features related to intermediate regions between the sub-objects in the object image.

Apparatus for segmenting an object comprising sub-objects

Image registration very often used to be a tedious task which had to be performed manually. According to an exemplary embodiment of the present invention, a non-rigid image registration by an iterative refinement process based on a combination of landmarks with similarity values is proposed. Advantageously, a very fast and robust method maybe provided.

Growing community of inventors

Hamburg, Germany

Astrid Ruth Franz