Ariadna Jiménez-Partinen

As traffic cameras become prevalent, the automatic analysis of traffic scenes presents new opportunities and challenges. Advances in deep learning allow for automated characterization of traffic in such videos. This work aims to understand traffic flow without human supervision, focusing on the localization of road intersections. For this purpose, a three-stage method is proposed that uses a deep neural network for vehicle detection, an object tracker to recover vehicle trajectories, and unsupervised machine learning to detect potential incoming and outgoing traffic flows. The approach has been tested on a variety of real and synthetic videos, with satisfactory results across different camera positions, traffic patterns, and weather conditions. As a key part of the methodology, five options for clustering starting and ending track points were tested. These options included a basic strategy based on predefined spatially localized clusters, and the K-means algorithm with two methods to determine the optimal number of clusters: the Elbow method and the Silhouette score. Additionally, Mean Shift and the Density-Based Spatial Clustering of Applications with Noise were evaluated. An exhaustive analysis of the proposed clustering methods was conducted, including runtime at each stage, performance metrics, and the addition of noise to simulate tracker failures. The results demonstrated the feasibility of the proposed methodology and concluded that Mean Shift is the most suitable clustering method due to its balance of high performance, low runtime, and stable behavior against abnormal trajectory points.

Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well‐developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high‐quality open‐access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease‐related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer‐aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning‐based methods and giving the scientific community a starting point to improve CAD detection.