Internal Body Marker Prediction From Surface Data In Medical Imaging
AssigneeSiemens Healthcare GmbH
InventorsBrian Teixeira, Vivek Kumar Singh, Birgi Tamersoy, Terrence Chen, Kai Ma, Andreas Krauss, Andreas Wimmer
Life of this patent
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Abstract
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.
Claims21
A method for internal body marker prediction from surface data in a medical imaging system, the method comprising:
- capturing, with a sensor, an outer surface of a patient, the surface data being from the capturing of the outer surface of the patient;
- generating, by an image processor, an image by a machine-learned network in response to input of the surface data to the machine-learned network, the image representing a location of the internal body marker of the patient;
- and
- displaying, by a display device, the image.
A method for internal body marker prediction from surface data in a medical imaging system, the method comprising:
- capturing, with a sensor, an outer surface of a patient, the surface data being from the capturing of the outer surface of the patient;
- generating, by an image processor, locations of a plurality of internal body markers of the patient by a machine-learned neural network in response to input of the surface data to the machine-learned neural network;
- and
- configuring a medical scanner to scan the patient based on the internal body markers.
A medical imaging system for internal anatomy prediction, the medical imaging system comprising:
- a depth sensor configured to measure depths to a patient;
- an image processor configured to apply a machine-learned neural network to depth information from the depths, the machine-learned neural network having been trained to predict locations of landmarks for the internal anatomy from the depth information;
- and
- a display configured to display an image showing the predicted locations of the landmarks.
Dependencies are inferred from the claim text.