Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Siemens Aktiengsellschaft

Siemens Medical Soultions Usa, Inc.

Shuping Qing

Hong Shen

Lin Hong

Benjamin L Odry

Tong Fang

Stefan Thesen

Gregory G Slabaugh

Gozde Unal

Valer Canda

Yonggang Shi

Andrew Litvin

Shawn Lankton

A system and method for enhanced viewing of rib metastasis in CT volume data is disclosed. The system and method receive input CT volume data and display slices of the CT volume data. Ribs are automatically segmented from the CT volume data, ordered and labeled. A 3D visualization of the ribs is generated and displayed. Alterations in the rib structure is automatically detected using shape based analysis of the ribs. The alterations are marked as candidate locations for rib metastasis in the displayed slices and 3D visualization in order to assist in the diagnosis of rib metastasis.

System and method for enhanced viewing of rib metastasis

Disclosed is a method and apparatus for registering data points in data sets representing scan data. Data points corresponding to a physical feature represented in the scan data are automatically detected. The detected data points in one data set are correlated with detected data points in another data set. A group of similarity transformations between the correlated detected data points is then calculated. The group of similarity transformations is then combined. In one advantageous embodiment, the physical feature is vertebras.

Method and system for registering CT data sets

A method for lesion segmentation in 3-dimensional (3D) digital images, includes selecting a 2D region of interest (ROI) from a 3D image, the ROI containing a suspected lesion, extending borders of the ROI to 3D forming a volume of interest (VOI), where voxels on the borders of the VOI are initialized as background voxels and voxels in an interior of the VOI are initialized as foreground voxels, propagating a foreground and background voxel competition where for each voxel in the VOI, having each neighbor voxel in a neighborhood of the voxel attack the voxel, and, if the attack is successful, updating a label and strength of the voxel with that of the successful attacking voxel, and evolving a surface between the foreground and background voxels in 3D until an energy functional associated with the surface converges in value, where the surface segments the suspected lesion from the image.

System and method for lesion segmentation in whole body magnetic resonance images

A method for performing computer-assisted lesion assessment includes acquiring contrast enhanced MRI data. A region of interest is identified. Kinetic properties, morphological properties and pattern of enhancement are determined for the identified region of interest. A score is calculated for each of the kinetic properties, morphological properties, pattern of enhancement. A cumulative score is calculated for the identified region of interest. The scores are provided to a medical practitioner to assist in diagnosing the identified region of interest.

Computer aided lesion assessment in dynamic contrast enhanced breast MRI images

A method for tracing, rib posteriors includes providing an incomplete rib tracing comprising a digitized 3-dimensional representation of a rib and a digital image from which said rib tracing was extracted, initializing, a tracing direction for a remainder of said rib, detecting a plurality of ridge points in a sub-volume of said digital image about said initial tracing direction, and deforming a closed curve in a plane perpendicular to said tracing direction until a rib-edge contour is obtained, using said ridge points as constraints.

System and method for tracing rib posterior in chest CT volumes

Disclosed is a systematic way of automatically segmenting lung regions. To increase the efficiency of a lung segmentation technique, a region-based technique, such as region growing, is performed by a computer on a middle slice of the CT volume. A contour-based technique is then used for a plurality of non-middle slices of the CT volume. This allows the implementation to be multithreaded and results in an improvement in the segmentation algorithm's efficiency.

Method and system for automatic lung segmentation

A method for jointly evaluating multi-phase magnetic resonance bone marrow images includes receiving a plurality of magnetic resonance (MR) image sequences of bones acquired using different protocols, each sequence comprising a plurality of images, each image comprising a plurality of intensities corresponding to a domain of points on a 2-dimensional grid, analyzing an image sequence to determine the MR protocol of said sequence, segmenting the bone marrow region in each image of said plurality of MR image sequences, and registering each MR image sequence to every other image sequence in said plurality of sequences wherein each point in each image of each of said plurality of image sequences is registered, wherein said registered image sequences are adapted to being analyzed synchronously.

System and method for the joint evaluation of multi phase MR marrow images

A method for analyzing medical image includes obtaining a plurality of surface fronts on an object, such points being disposed in a three dimensional volume in the object; varying the volume of each one of the points such volumes combining into a common volume; and terminating the process when the common volume reaches boundaries of the object.

Method for analyzing medical image data using level set

In a method of 3-D object detection, an image is pre-processed. Using cross-section analysis, a confidence array is built. A plurality of peaks in the confidence array are detected, wherein the peaks signify a likelihood of a 3-D object of interest.

Object detection using cross-section analysis

A computer-implemented method for candidate generation in three-dimensional volumetric data comprises forming a binary volumetric image of the three-dimensional volumetric data including labeled foreground voxels, estimating a plurality of shape features of the labeled foreground voxels in the binary volumetric data including, identifying peak voxels and high curvature voxels from the foreground voxels in the binary volumetric image, accumulating a plurality of confidence values for boundary and each peak voxel, and detecting confidence peaks from the plurality of confidence values, wherein the confidence peaks are determined to be the candidate points, and refining the candidate points given detected confidence peaks, wherein refined candidate points are determined to be candidates.

Candidate generation for lung nodule detection

A computer-implemented method for building an atlas includes providing a volume data for a plurality of bones, segmenting, individually, the plurality of bones from the volume data, storing locations of a plurality of voxels for each of the plurality of bones, wherein each of the plurality of bones is registered with a dedicated list of locations, wherein the locations are locations in a coordinate system of the volume data, selecting one or more of the plurality of bones, forming a selected structure, and rendering the selected structure in the coordinate system of the volume data according to the dedicated list of locations of each of the plurality of bones in the selected structure, wherein a rendering is displayed by a display.

Interactive atlas extracted from volume data

A method of identifying a patient from digital medical images includes providing a first digital image volume of an organ of a patient and a second digital image volume of the same organ, segmenting each slice of the first image volume and calculating a cross-sectional area of the organ in each slice to form a first area profile, segmenting each slice of the second image volume and calculating a cross-sectional area of the organ in each slice to form a second area profile, and comparing the first area profile with the second area profile to determine a correlation value for the two profiles. Based on the correlation value between the first area profile and the second area profile, it is determined whether the first digital image volume of the organ and the second digital image volume of the same organ came from the same patient.

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Princeton, NJ, United States of America

Shuping Qing