Unsupervised Anomaly Detection Framework for Coronary Artery Lesions in CTCA

Coronary artery disease is a leading cause of mortality world-wide, emphasizing the need for accurate and efficient detection methods. Current deep learning approaches for coronary lesion detection in computed tomography coronary angiog-raphy (CTCA) rely on large labeled datasets for supervised learning, limiting scalability. This study proposes an unsu-pervised framework for coronary lesion detection in CTCA. Using vessel linearization, we generated small linear volume patches from CTCA and trained a deep autoencoder model, MemAE, exclusively on normal patches to detect anomalies based on reconstruction error without lesion location labels. Results on a public dataset demonstrate high accuracy, achieving 97.5% on per-patient average reconstruction error and 85% using the number of patches with high reconstruction error, offering a scalable and accessible solution.