Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Maria Jimena Costa

Michael Kelm

Dorin Comaniciu

Shaohua Kevin Zhou

Michael Suehling

Michael Wels

Martin Huber

Alexey Tsymbal

Sascha Seifert

Alexander Cavallaro

Yefeng Zheng

David D Liu

Anna Jerebko

Christian Tietjen

Olivier Pauly

Dijia Wu

Helmut König

Roland Brill

Rüdiger Bertsch

Alexander Schwing

A patient image record and a reference image record are read for capturing anatomical features of a patient on the basis of medical image records is provided. Joints are localized in the patient image record and in the reference image record by means of a pattern recognition method. At the localized joints, the patient image record is segmented into patient image segments and the reference image record is segmented into reference image segments. According to the invention, a segment-individual image deformation is established for a respective reference image segment or patient image segment, by means of which image deformation image structures of the reference image segment are mapped to corresponding image structures of the patient image segment). Then, an anatomical feature is captured on the basis of the segment-individual maps between the reference image segments) and the patient image segments.

Medical image processing method and image processing system

A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.

Generating a synthetic two-dimensional mammogram

A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Computer-aided evaluation of an image dataset

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 fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

Method and system for segmentation of the prostate in 3D magnetic resonance images

A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.

Image processing using random forest classifiers

Growing community of inventors

Nuremberg, Germany

Maria Jimena Costa