Siemens Healthineers Ag

Sailesh Conjeti

Christian Huemmer

Alexander Preuhs

Sven-Martin Sutter

Alexis Laugerette

Lei Wang

Christian Hümmer

Techniques are described to infer 2-D segmentations of a region of interest using a neural network algorithm. Techniques are described to train the neural network algorithm. The 2-D segmentations are determined based on multiple 2-D projection images. For example, x-ray images can be used as an input.

Processing 2-D projection images using a neural network

A method for evaluation of medical image data comprises: providing medical image data of a patient to be examined; determining, for at least one segment of the medical image data, a respective classification probability value with respect to at least one classification from a list of specified classifications; determining a patient-specific relevance criterion for at least one classification for at least the at least one segment of the medical image data; and determining a clinical relevance of the at least one classification for the at least one segment of the medical image data using the patient-specific relevance criterion, and at least one of based on the classification probability values or based on the at least one segment of the medical image data.

Method and apparatus for the evaluation of medical image data

A method for automatically analysing 2D medical image data, including an additional object comprises acquiring the 2D medical image data from an examination portion of a patient using a first modality; acquiring additional image data from the examination portion using a different modality; and performing an automatic image analysis based on the acquired 2D medical image data and the acquired additional image data, the image analysis being adapted to the additional object.

Automatic analysis of 2D medical image data with an additional object

A computer-implemented method is for providing at least one first metadata attribute associated with a medical image. The method includes receiving the medical image and the at least one first metadata attribute. Therein the at least one first metadata attribute includes an attribute tag and a provisional attribute value. Furthermore, the method includes applying a first trained function to the medical image to determine an image-based attribute value. Furthermore, the method includes determining a final attribute value based on the provisional attribute value and the image-based attribute value. Furthermore, the method includes providing the at least one first metadata attribute. Therein the at least one first metadata attribute includes the attribute tag and the final attribute value.

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Erlangen, Germany

Sailesh Conjeti