Abstract: A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

Abstract: A computer-implemented method for comparing follow-up medical findings includes receiving image data showing a body part of a patient at a first time and retrieving, from a database, reference data associated to the image data. The reference data includes reference biometric data and one or more reference medical findings. Thereby the reference biometric data and the one or more reference medical findings have been extracted from reference image data depicting the body part of the patient at a second time, different than the first time. The method further includes processing the received image data to extract, from the image data, biometric data corresponding to the reference biometric data and one or more medical findings; performing a registration of the extracted biometric data with the reference biometric data; and comparing the extracted medical findings with the reference medical findings using the registration.

Abstract: Techniques of storing and retrieving medical data and information are provided. Medical result information can be stored on a long-term data repository in a shared network, e.g., the Internet. Pseudonymized identifiers of patients can be used to retrieve such data and information.

Abstract: A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Abstract: At least one example embodiment relates to a computer-implemented method comprising receiving a medical image dataset; receiving at least one medical comparison image dataset; extracting biometric data based on the medical image dataset; extracting biometric comparison data based on the medical comparison image dataset; determining a measure of difference between the biometric comparison data and the biometric data; and assigning the medical image dataset to the medical comparison image dataset when the measure of difference does not exceed a threshold value.

Abstract: A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

Abstract: Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Abstract: 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.

Abstract: A computer-implemented method for comparing follow-up medical findings includes receiving image data showing a body part of a patient at a first time and retrieving, from a database, reference data associated to the image data. The reference data includes reference biometric data and one or more reference medical findings. Thereby the reference biometric data and the one or more reference medical findings have been extracted from reference image data depicting the body part of the patient at a second time, different than the first time. The method further includes processing the received image data to extract, from the image data, biometric data corresponding to the reference biometric data and one or more medical findings; performing a registration of the extracted biometric data with the reference biometric data; and comparing the extracted medical findings with the reference medical findings using the registration.

Abstract: A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Abstract: Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Abstract: 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.