Siemens Healthcare Gmbh

Siemens Healthineers Ag

Anamaria Vizitiu

Lucian Mihai Itu

Puneet Sharma

Shaohua Kevin Zhou

Rui Liao

Daguang Xu

Dominik Neumann

Olivier Pauly

Siqi Liu

Jan Kretschmer

Olivier Ecabert

Philipp Seegerer

Alexandru Turcea

Laszlo Lazar

Venkatesh Narasimha Murthy

Iulian Ionut Stroia

Walid Bekhtaoui

Andreea Bianca Popescu

Cosmin Ioan Nita

Ioana Taca

Klaus Kirchberg

Lulian Ionut Stroia

The present application relates to a computer-implemented dynamic vessel roadmapping method. The method comprises generating a vessel roadmap library, the vessel roadmap library including a plurality of vessel roadmaps, wherein each vessel roadmap comprises vessel roadmap image and first and second alignment data, obtaining a real-time fluoroscopy image and corresponding real-time first and second fluoroscopy information, overlaying a vessel roadmap image of the vessel roadmap library with the real-time fluoroscopy image based on the first alignment data, the second alignment data, and the real time first and second fluoroscopy information and aligning the vessel roadmap image and the real-time fluoroscopy image based on the second alignment data and the real time second fluoroscopy information, wherein the second alignment data is derived from the corresponding vessel roadmap image.

Dynamic vessel roadmapping

Data privacy is a major concern when accessing and processing sensitive medical data. Homomorphic Encryption (HE) is one technique that preserves privacy while allowing computations to be performed on encrypted data. An encoding method enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size by representing the numbers as a series of polynomial terms.

Privacy preserving artificial intelligence based clinical decision support

A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.

Semantic segmentation for cancer detection in digital breast tomosynthesis

A method is for segmenting an object in a medical image with a plurality of iteration steps. In an embodiment of the method, each iteration step includes generating a plurality of patches, a portion of the input image and a patch location being assigned to each patch, the patch location being indicative of the location of the portion of the input image relative to the input image. For each patch of the plurality of patches, the method includes determining a vote location based on the portion of the input image assigned to that patch and determining a target location based on the vote location and the patch location assigned to that patch. Finally, in an embodiment the method includes generating a vote map, each patch of the plurality of patches contributing to a pixel value at the target location of the patch in the vote map.

Method and data processing unit for segmenting an object in a medical image

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Sfantu Gheorghe, Romania

Anamaria Vizitiu