Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images

TL;DR

This paper presents a web-based inpainting tool that uses neural networks to virtually transform dissected aortae into healthy ones in angiography images, aiding medical diagnosis and research.

Contribution

It introduces a user-friendly web application integrating neural network inpainting for aortic images, facilitating easier analysis and virtual disease regression.

Findings

Successfully integrated neural network inpainting into a web viewer.

Simplified user interface for medical image analysis.

Potential to generate healthy-dissected image pairs for research.

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.