Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images
This tool addresses the lack of healthy-dissected image pairs for aortic dissection patients, enabling virtual regression for disease study, but it is incremental as it applies existing inpainting methods to a new medical domain.
The researchers tackled the problem of generating healthy aortic images from dissected ones for medical monitoring by developing a web-based inpainting tool that combines a neural network with a user interface, achieving integration into a 3D medical image viewer to simplify usage.
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.