Stenosis detection in coronary X-ray angiographies

Automatic processing of images from coronary X-ray angiographies using deep learning techniques has been explored, and the results show that it is possible to perform high-quality segmentation of relevant coronary arteries. Building on top of existing segmentation methods, based on deep convolutional neural networks, this dissertation will be focused on the estimation of the value of the instantaneous wave-free ratio (iFR) and/or the Fractional Flow Reserve (FFR) index from segmented images. The objective is to develop a methodology that can estimate the value of the iFR using non-invasive procedures and that has sufficient sensitivity to avoid the need for invasive measurement methods, such as the insertion of a guidewire with a pressure sensor inserted through a coronary catheter. Estimating the iFR and the FFR indexes is a difficult task, since imaging data, even after segmentation, will provide insufficient information, in many cases. Exploration of the possible tradeoffs between positive predictive value and recall will play an essential role in the identification of the best approach. Co-supervisors: Miguel Nobre Menezes (20%), João Lourenço Silva (40%) Requisites: The student should have significant programming experience, and practical knowledge of machine learning languages and environments, such as PyTorch or TensorFlow. He/she should also have interest in developing the understanding of medical image processing and cardiology. Notes: This work will be developed in cooperation with the school of department of cardiology of the School of Nedicine of the University of Lisbon. The selected student will have access to the facilities of INESC-ID and the MLKD group (https://mlkd.idss.inesc-id.pt/), including computing facilities that include four DELL PowerEdge C41402 servers, eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB Tesla A100, among other computing servers.