Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.

Journal: Cardiovascular engineering and technology
PMID: 38388764

Abstract

PURPOSE: Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algorithms, which in turn allow the prediction of possible complications and enable risk assessment, which is crucial in saving lives. The aim of this paper is to present a novel fully automatic 3D segmentation method, which combines basic image processing techniques and more advanced machine learning algorithms, for detecting and modelling the aorta in 3D CT imaging data.

Authors

  • Christos Mavridis
    Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Athens, Greece. chmavridis@biomed.ntua.gr.
  • Theodore L Economopoulos
    Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Athens, Greece.
  • Georgios Benetos
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: Georgios.benetos@usz.ch.
  • George K Matsopoulos
    Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.

Keywords

External Resources

Popular Topics
Recent Journals