Siemens Healthcare Gmbh

Siemens Healthineers Ag

Yu Zhao

Gerardo Hermosillo Valadez

Xiang Sean Zhou

Yiqiang Zhan

Yoshihisa Shinagawa

Liang Zhao

Matthias Wolf

Zhigang Peng

Parmeet Singh Bhatia

Shu Liao

Anna Jerebko

Simon Allen-Raffl

Zhennan Yan

Parmeet Bhatia

Ke Zeng

Yiyuan Zhao

Yimo Gao

Yimo Guo

Chen Li

Katharina Schmidler Burk

Halid Yerebakan

Disclosed is a computer-implemented method for detecting one or more anatomic landmarks in medical image data. In an embodiment, the method includes receiving a medical image data set depicting a body part of a patient; and determining a first set of anatomic landmarks from a first representation of the medical image data set at a first resolution by applying a first trained function to the first representation of the medical image data set. Based on that, a second set of anatomic landmarks is determined from a second representation of the medical image data set at a second resolution, the second resolution being higher than the first resolution, by applying a second trained function different than the first trained function to the second representation of the medical image data set and using the first set of landmarks by the second trained function.

Method and system for detecting landmarks in medical images

Disclosed is a method, a computer readable storage medium and an apparatus for processing medical image data. Input medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system on the input medical image data to identify a volume of interest within which an instance of a predetermined anatomical structure is located. First and second determination processes are performed at the data processing system to determine, respectively, first and second anatomical directions for the instance of the anatomical structure that are defined relative to the coordinate system of the input medical image data. Output data relating to the first and second anatomical directions is output from the data processing system.

Medical image data

A framework for cross-modality image synthesis. A first and second model may be trained using respective first and second pairs of complementary images and corresponding first and second ground truth images that represent first and second views of a region of interest. The first and second pairs of complementary images may be acquired by a first modality and the first and second ground truth images may be acquired by a second modality. A combinational network may further be trained to combine features from the first and second models. At least one synthetic second modality image may then be generated by passing current images through the trained first or second model and the combinational network, wherein the current images are acquired by the first modality and represent the first or second view of the region of interest.

Cross-modality image synthesis

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Athens, GA, United States of America

Yu Zhao