Siemens Medical Solutions Usa, Inc.

Siemens Healthineers Ag

Fei Gao

Sasa Grbic

Zhoubing Xu

Dominik Neumann

Bruce S Spottiswoode

Vijay Shah

Günther Platsch

Guillaume Chabin

Methods and systems of revising image models for nuclear imaging are disclosed. A system receives first patient scan data corresponding to one or more nuclear imaging scans performed on one or more individuals and generates a first reconstructed image by applying a first image model to the first patient scan data. Feedback is received regarding the first reconstructed image and the feedback is provided as an input to a deep-reinforcement learning process. The deep-reinforcement learning process is configured to generate at least one modification for the first image model based on the feedback regarding the first reconstructed image. A second image model is generated by applying the at least one modification generated by the deep-reinforcement learning process to the first image model.

Deep reinforcement learning for computer assisted reading and analysis

The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

Method and arrangement for automatically localizing organ segments in a three-dimensional image

Systems and methods of image reconstruction are disclosed. First image scan data corresponding to a first imaging modality is obtained. One or more deep learning processes are applied to the first image scan data, where the one or more deep learning processes are trained based on first image scan data corresponding to a second imaging modality, to generate second image scan data corresponding to the first imaging modality. An image is then reconstructed based on the first image scan data and the second image scan data corresponding to the first imaging modality.

Methods and apparatus for deep learning based data transfer between imaging systems

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Knoxville, TN, United States of America

Fei Gao