Abstract
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
Original language | English |
---|---|
Title of host publication | 2020 Central European Seminar on Computer Graphics |
Publication status | Published - 2020 |
Event | 24th Central European Seminar on Computer Graphics: CESCG 2020 - Virtuell, Slovakia Duration: 30 Apr 2020 → 4 May 2020 |
Conference
Conference | 24th Central European Seminar on Computer Graphics |
---|---|
Country/Territory | Slovakia |
City | Virtuell |
Period | 30/04/20 → 4/05/20 |
Prizes
-
2nd Best Presentation Award
Prutsch, Alexander (Recipient), Pepe, Antonio (Recipient) & Egger, Jan (Recipient), 2020
Prize: Prizes / Medals / Awards