Self-learning distributed system with automated ground-truth generation

Abstract

In order to generate annotated ground truth data for training a machine learning model for inferring a desired scan configuration of an medical imaging system from an observed workflow scene during exam preparation, a system is provided that comprises a sensor data interface configured to access a measurement image of a patient positioned for an imaging examination. The measurement image is generated on the basis of sensor data obtained from a sensor arrangement, which has a field of view including at least part of an area, where the patient is positioned for imaging. The system further comprises a medical image data interface configured to access a medical image of the patient obtained from a medical imaging apparatus during the imaging examination. The patient is positioned in a given geometry with respect to a reference coordinate system of the medical imaging apparatus. The system further comprises an exam metadata interface configured to access exam metadata of the imaging examination. The system further comprises a processing unit, configured to determine an association between one or more features in the measurement image and one or more features extracted from the medical image and/or from the exam metadata by mapping a point in a coordinate system of the medical image to a point in a coordinate system of the measurement image. The system further comprises an output interface, configured to be coupled to a training set database for adding the measurement image comprising data that labels the one or more associated features in the measurement image to the training set database for training the machine learning model.