Autor e vínculos: Professor Arlindo Oliveira; Área Científica: Metodologia e Tecnologias da Programação (MTP)

Título: Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
Abstract: Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical features for further analysis by quantitative methods. We present the results obtained using different deep learning architectures for coronary artery segmentation and suggest some possible applications. These architectures have been shown to be useful in other segmentation problems in the medical domain.