Siemens Healthcare Gmbh

Siemens Healthineers Ag

Brian Teixeira

Vivek Kumar Singh

Birgi Tamersoy

Dorin Comaniciu

Bogdan Georgescu

Terrence Chen

Sasa Grbic

Zhoubing Xu

Kai Ma

Andreas Krauss

Youngjin Yoo

Siqi Liu

Thomas Re

Sebastien Piat

Guillaume Chabin

Shikha Chaganti

Thomas Flohr

Ankur Kapoor

Andreas Wimmer

Yifan Wu

Andreas Krauß

Valentin Ziebandt

For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Body representations

Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.

Medical image synthesis of abnormality patterns associated with COVID-19

Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

Assessment of abnormality regions associated with a disease from chest CT images

Synthetic CT is estimated for planning or other purposes from surface data (e.g., depth camera information). The estimation uses parameterization, such as landmark and/or segmentation information, in addition to the surface data. In training and/or application, the parameterization may be used to correct the predicted CT volume. The CT volume may be predicted as a sub-part of the patient, such as estimating the CT volume for scanning one system, organ, or type of tissue separately from other system, organ, or type of tissue.

Synthetic parameterized computed tomography from surface data in medical imaging

For topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates the topogram representing an interior organ based on the outside surface of the patient. To further adapt to specific patients, internal landmarks are used in the topogram prediction. The topogram generated by one generator of the GAN may be altered based on landmarks generated by another generator.

Topogram prediction from surface data in medical imaging

For anomaly detection based on topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates a topogram representing an interior anatomy based on the outside surface of the patient. An X-ray image of the patient is acquired and compared to the generated topogram. By quantifying the difference between the real X-ray image and the predicted one, anatomical anomalies may be detected.

Anomaly detection using parametrized X-ray images

Machine learning is used to train a network to predict the location of an internal body marker from surface data. A depth image or other image of the surface of the patient is used to determine the locations of anatomical landmarks. The training may use a loss function that includes a term to limit failure to predict a landmark and/or off-centering of the landmark. The landmarks may then be used to configure medical scanning and/or for diagnosis.

Internal body marker prediction from surface data in medical imaging

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Verneuil-en-Halatte, France

Brian Teixeira