The Kawasaki disease is the most common heart condition affecting young children, usually under five years old, in developed countries. The disease is responsible for the damages of blood vessels all over the body and results in vasculitis, myocarditis and coronary dilation causing long term heart complications. Therefore, it is essential to be able to detect the disease at an early state. One of the methods used to detect Kawasaki disease is by the analysis of the echocardiograms of the heart. In the Grupo de Tratamiento de Imágenes we have already developed a platform to assist Kawasaki disease diagnosis. This Mater Thesis addresses the design and implementation of an annotation tool for the echocardiograms, and its integration with the platform. This Master Thesis will be done in collaboration with Hospital 12 de Octubre. We are looking for students with experience with Python. Experience with Deep Learning Tools ( Keras, Tensorflow, PyTorch, …) is a plus.
Responsable: Julián Cabrera [julian.cabrera@gti.ssr.upm.es]
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