Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Product Lifecycle Management Software Inc.

Siemens Healthcare Gmbh

Dijia Wu

Shaohua Kevin Zhou

David D Liu

Grzegorz Soza

Meizhu Liu

Dorin Comaniciu

Michal Sofka

Christian Tietjen

Le Lu

Michael Suehling

Neil Birkbeck

Christopher Vincent Alvino

Atilla Peter Kiraly

Yoshihisa Shinagawa

Marcos Salganicoff

Michael Kelm

Michael Wels

Clifford R Weiss

Jinbo Bi

Maria Jimena Costa

Quan Wang

Kevin Shaohua Zhou

Hao Xu

Sowmya Ramakrishnan

Qian Wang

A method and system for automatic bone segmentation and landmark detection for joint replacement surgery is disclosed. A 3D medical image of at least a target joint region of a patient is received. A plurality bone structures are automatically segmented in the target joint region of the 3D medical image and a plurality of landmarks associated with a joint replacement surgery are automatically detected in the target joint region of the 3D medical image. The boundaries of segmented bone structures can then be interactively refined based on user inputs.

Method and system for bone segmentation and landmark detection for joint replacement surgery

A method and system for automatic pelvis unfolding from 3D computed tomography (CT) images is disclosed. A 3D medical image, such as a 3D CT image, is received. Pelvis anatomy is segmented in the 3D medical image. The 3D medical image is reformatted to visualize an unfolded pelvis based on the segmented pelvis anatomy.

Method and system for automatic pelvis unfolding from 3D computed tomography images

Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.

Multi-bone segmentation for 3D computed tomography

A method and apparatus for automatic spinal canal segmentation in medical image data, such as computed tomography (CT) image data, is disclosed. An initial set of spinal canal voxels is detected in the 3D medical image using a trained classifier. A spinal canal topology defined by a current set of spinal canal voxels is refined based on an estimated medial line of the spinal canal. Seed points are sampled based on the refined spinal canal topology. An updated set of spinal canal voxels is detected in the 3D medical image using random walks segmentation based on the sampled seed points. The spinal canal topology refinement, seed points sampling, and random walks segmentation are repeated in order to provide cascaded random walks segmentation to generate a final spinal canal segmentation result.

Automatic spinal canal segmentation using cascaded random walks

Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

Automatic spatial context based multi-object segmentation in 3D images

A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.

Method and system for liver lesion detection

A method and system for up-vector detection for ribs in a 3D medical image volume, such as a computed tomography (CT) volume is disclosed. A rib centerline of at least one rib is extracted in a 3D medical image volume. An up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib. The up-vector at each centerline point can be detected using a trained regression function. Alternatively, the up-vector at each centerline point can be detected by detecting an ellipse shape in a cross-sectional rib image generated at each centerline point.

Method and system for up-vector detection for ribs in computed tomography volumes

A method and system for extracting rib centerlines in a 3D volume, such as a 3D computed tomography (CT) volume, is disclosed. Rib centerline voxels are detected in the 3D volume using a learning based detector. Rib centerlines or the whole rib cage are then extracted by matching a template of rib centerlines for the whole rib cage to the 3D volume based on the detected rib centerline voxels. Each of the extracted rib centerlines are then individually refined using an active contour model.

Method and system for automatic rib centerline extraction using learning based deformable template matching

Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.

Multi-level contextual learning of data

A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.

Method and system for automatic detection of spinal bone lesions in 3D medical image data

Ribs are automatically ordered and paired. After ordering ribs on each side, magnetic and spring functions are used to solve for rib pairing. The magnetic function is used to constrain possible pairs across sides, and the spring function is used to maintain the order on each side while accounting for missing or fused ribs.

Growing community of inventors

North Brunswick, NJ, United States of America

Dijia Wu