Machine Learning/Deep Learning
Artifact detection and quantification
MR image artifacts can degrade the perceived image quality and even hamper a reliable diagnosis. It is therefore crucial to automatically detect and quantify these artifacts in a prospective manner (quality assurance) or in a retrospective setting (quality control).
Automatic Image Quality Assessment
An automatic non-reference/blind medical image quality assessment system to estimate human observer scores based on an underlying diagnostic question/application/task.
An easy-to-use and accessible labeling platform helps to assess the needed labels to mimic the human visual system by a model observer.
Images can be described by other characteristics or attributes which can be calculated via a feature toolbox.
Semantic segmentation
Automatic segmentation of MR and CT images in defined tissues/organs.
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