Siemens Healthcare Gmbh

Guillaume Chabin

Sasa Grbic

Dorin Comaniciu

Zhoubing Xu

Bogdan Georgescu

Siqi Liu

Youngjin Yoo

Thomas Re

Brian Teixeira

Sebastien Piat

Shikha Chaganti

Thomas Flohr

Razvan Ionasec

Eli Gibson

Arnaud Arindra Adiyoso

Jonathan Sperl

Amitkumar Bhupendrakumar Shah

Valentin Ziebandt

Rainer Kärgel

Jie Yang

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

For processing a medical image, medical image data representing a medical image of at least a portion of a vertebral column is received. The medical image data is processed to determine a plurality of positions within the image. Each of the plurality of positions corresponds to a position relating to a vertebral bone within the vertebral column. Data representing the plurality of positions is processed to determine a degree of deformity of at least one vertebral bone within the vertebral column.

Determination of a degree of deformity of at least one vertebral bone

Systems and methods are provided for generating a synthesized medical image patch of a nodule. An initial medical image patch and a class label associated with a nodule to be synthesized are received. The initial medical image patch has a masked portion and an unmasked portion. A synthesized medical image patch is generated using a trained generative adversarial network. The synthesized medical image patch includes the unmasked portion of the initial medical image patch and a synthesized nodule replacing the masked portion of the initial medical image patch. The synthesized nodule is synthesized according to the class label. The synthesized medical image patch is output.

Class-aware adversarial pulmonary nodule synthesis

Systems and methods for determining whether a bone of a patient is injured are provided. A medical image of a bone of a patient is received. A synthesized bone image is generated over the bone in the medical image to provide a reconstructed image. The synthesized bone image represents uninjured bone. The medical image is compared with the reconstructed image to evaluate an injury to the bone of the patient.

Analysis of skeletal trauma using deep learning

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Princeton, NJ, United States of America

Guillaume Chabin