Segmentation, Medical Imaging, Machine Learning for Medical Applications
Optical coherence tomography (OCT), employing light rather than ultrasound, is a high-resolution imaging technology that permits a precise assessment of biological tissue. Used intravascular, OCT is increasingly used for assessing safety and efficacy of intracoronary devices, such as drug-eluting stents and bioabsorbable stents. Obtained images provide insights regarding stent malposition, overlap, and neointimal thickening, among others. Recent OCT histopathology correlation studies have shown that OCT can be used to identify plaque composition, and hence it is possible to distinguish “normal” from “abnormal” neointimal tissue based on its visual appearance. The aim of this project is to develop a novel method for the automatic analysis of tissue in IVOCT. Automatic tissue classification will allow for a quantitative and potentially more time-efficient and objective analysis of IVOCT data. For instance, classifying neointimal tissue as either “mature” or “immature” can be used to assess the disease state of patients; as a potential predictor of late stent-failure events such as stent thrombosis and restenosis.
Visit our lab at Garching.
Visit our lab at Deutsches Herzzentrum Muenchen.
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Please contact Sailesh Conjeti
, Amin Katouzian
, for available student projects within this research project.