Evaluation of Image Processing Methods for Clinical Applications: Mimicking Clinical Data Using Conditional GANs

Abstract

While developing medical image applications, their accuracy is usually evaluated on a validation dataset, that generally differs from the real clinical data. Since clinical data does not contain ground truth annotations, it is impossible to approximate the real accuracy of the method. In this work, a cGAN-based method to generate realistically looking clinical data preserving the topology and thus ground truth of the validation set is presented. On the example of image registration of brain MRIs, we emphasize the necessity for the method and show that it enables evaluation of the accuracy on a clinical dataset. Furthermore, the topology preserving and realistic appearance of the generated images are evaluated and considered to be sufficient.

OriginalspracheEnglisch
Titel Bildverarbeitung für die Medizin 2019
Seitenumfang6
Herausgeber (Verlag)Springer Verlag
Erscheinungsdatum01.01.2019
Seiten15-20
ISBN (Print)978-3-658-25325-7
ISBN (elektronisch)978-3-658-25326-4
DOIs
PublikationsstatusVeröffentlicht - 01.01.2019
VeranstaltungWorkshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Deutschland
Dauer: 17.03.201919.03.2019
Konferenznummer: 224899

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