Abstract: One or more example embodiments of the present invention is based on a computer-implemented method for providing molecular data. The method comprises receiving a computed tomography image of at least a part of a lung of a patient, wherein the computed tomography image depicts at least one lung nodule. The molecular data is determined by processing first input data with a first trained function, wherein the first input data is based on the computed tomography image, and wherein the molecular data relates to a biomarker within at least one of a genome of the patient, a transcriptome of the patient, a proteome of the patient or a metabolome of the patient. Furthermore, the molecular data is provided. Providing the molecular data can comprise at least one of displaying, transmitting or storing the molecular data.

Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Abstract: Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.

Abstract: A sensor selection facility is described. In an embodiment, the sensor selection facility includes an image capture unit for acquiring image data from a patient; a position ascertainment unit for ascertaining positions of the capacitive sensor electrodes relative to the body of the patient based upon the image data; an evaluation unit for ascertaining the anticipated quality of a sensor signal from the capacitive sensor electrodes based upon the ascertained positions; and a combination unit for defining a combination strategy for combining the sensor signals from the respective capacitive sensor electrodes based upon the ascertained signal quality of the capacitive sensor electrodes. A differential voltage measurement system is also described. A method and computer readable medium for adapting a differential voltage measurement system are moreover described.

Abstract: A volume of interest is ultrasonically imaged. An object of interest is automatically located from a volume scan. In one approach, a geometric bounding box surrounding the object is found by a classifier. In another approach, an option for zooming to the object is indicated to the user. A scan region is defined around the object or the bounding box automatically, whether in response to user selection of the option or not. The scan region is shaped based on the ultrasound scan format, but is smaller than the volume. The volume of interest defined by the scan region is used to generate images with a greater temporal and/or spatial resolution than scanning of the entire original volume.

Abstract: A probe pose is detected in fluoroscopy medical imaging. The pose of the probe through a sequence of fluoroscopic images is detected. The detection relies on an inference framework for visual tracking overtime. By applying visual tracking, the pose through the sequence is consistent or the pose at one time guides the detection of the probe at another time. Single frame drop-out of detection may be avoided. Verification using detection of the tip of the probe and/or weighting of possible detections by separate detection of markers on the probe may further improve the accuracy.

Abstract: A probe pose is detected in fluoroscopy medical imaging. The pose of the probe through a sequence of fluoroscopic images is detected. The detection relies on an inference framework for visual tracking overtime. By applying visual tracking, the pose through the sequence is consistent or the pose at one time guides the detection of the probe at another time. Single frame drop-out of detection may be avoided. Verification using detection of the tip of the probe and/or weighting of possible detections by separate detection of markers on the probe may further improve the accuracy.

Abstract: A volume of interest is ultrasonically imaged. An object of interest is automatically located from a volume scan. In one approach, a geometric bounding box surrounding the object is found by a classifier. In another approach, an option for zooming to the object is indicated to the user. A scan region is defined around the object or the bounding box automatically, whether in response to user selection of the option or not. The scan region is shaped based on the ultrasound scan format, but is smaller than the volume. The volume of interest defined by the scan region is used to generate images with a greater temporal and/or spatial resolution than scanning of the entire original volume.