  Medical imaging is an important tool for the diagnosis and the evaluation of
an aortic dissection (AD); a serious condition of the aorta, which could lead
to a life-threatening aortic rupture. AD patients need life-long medical
monitoring of the aortic enlargement and of the disease progression, subsequent
to the diagnosis of the aortic dissection. Since there is a lack of
'healthy-dissected' image pairs from medical studies, the application of
inpainting techniques offers an alternative source for generating them by doing
a virtual regression from dissected aortae to healthy aortae; an indirect way
to study the origin of the disease. The proposed inpainting tool combines a
neural network, which was trained on the task of inpainting aortic dissections,
with an easy-to-use user interface. To achieve this goal, the inpainting tool
has been integrated within the 3D medical image viewer of StudierFenster
(www.studierfenster.at). By designing the tool as a web application, we
simplify the usage of the neural network and reduce the initial learning curve.
