Siemens Healthcare Gmbh

He Zhang

Shaohua Kevin Zhou

Zhoubing Xu

Mingqing Chen

Sasa Grbic

David D Liu

Shun P Miao

Daguang Xu

Jan Kretschmer

Dong Yang

Max Schöbinger

Tae Soo Kim

Sebastian Seifert

Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.

Hierarchical learning of weights of a neural network for performing multiple analyses

A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

Deep learning based bone removal in computed tomography angiography

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Edison, NJ, United States of America

He Zhang