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Abstract #1903

An Active Learning platform for automatic MR image quality assessment

Thomas Küstner1,2, Martin Schwartz1,2, Annika Kaupp2, Petros Martirosian1, Sergios Gatidis1, Nina F. Schwenzer1, Fritz Schick1, Holger Schmidt1, and Bin Yang2

1University Hospital Tübingen, Tübingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Acquired images are usually analyzed by a human observer (HO) according to a certain diagnostic question. Flexible algorithm parametrization and the enormous amount of data created per patient make this task time-demanding and expensive. Furthermore, definition of objective quality criterion can be very challenging, especially in the context of a missing reference image. In order to support the HO in assessing image quality, we propose a non-reference MR image quality assessment system based on a machine-learning approach with an Active Learning loop to reduce the amount of necessary labeled training data. Labeling is performed via an easy accessible website.

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