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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.
|Title of host publication
|Bildverarbeitung für die Medizin 2019
|Number of pages
|Published - 01.01.2019
|Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Germany
Duration: 17.03.2019 → 19.03.2019
Conference number: 224899
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