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

• Published in: Cardiovascular engineering and technology 2024-06, Vol.15 (3), p.359-373
• Main Authors: Mavridis, Christos, Economopoulos, Theodore L., Benetos, Georgios, Matsopoulos, George K.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aorta; Aorta - diagnostic imaging; Aortic Diseases - diagnostic imaging; Aortography; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Cardiology; Clinical medicine; Computed tomography; Computed Tomography Angiography; Coronary vessels; Fields (mathematics); Humans; Image processing; Image segmentation; Imaging, Three-Dimensional - methods; Machine Learning; Markov Chains; Medical imaging; Methods Paper; Performance evaluation; Predictive Value of Tests; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Risk assessment; Three dimensional models
• ISSN: 1869-408X; 1869-4098; 1869-4098
• DOI: 10.1007/s13239-024-00720-7

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. Methods An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach, based on a Markov Random Field network. The result of the proposed two-stage segmentation process is modelled and visualized. Results The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D models. The performance of segmentation was evaluated both qualitatively and quantitatively against other commonly used segmentation techniques, in terms of the accuracy achieved, compared to the actual aorta, which was defined manually by experts. Conclusion The proposed methodology achieved superior segmentation performance, compared to all compared segmentation techniques, in terms of the accuracy of the extracted 3D aortic model. Therefore, the proposed segmentation scheme could be used in clinical practice, such as in treatment planning and assessment, as it can speed up the evaluation of the medical imaging data, which is commonly a lengthy and tedious process.