Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.
Christos MavridisTheodore L EconomopoulosGeorgios BenetosGeorge K MatsopoulosPublished in: Cardiovascular engineering and technology (2024)
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.
Keyphrases
- deep learning
- convolutional neural network
- machine learning
- artificial intelligence
- clinical practice
- aortic valve
- big data
- healthcare
- pulmonary artery
- high resolution
- computed tomography
- heart failure
- left ventricular
- magnetic resonance imaging
- contrast enhanced
- electronic health record
- image quality
- mass spectrometry
- dual energy
- photodynamic therapy
- fluorescence imaging
- pulmonary arterial hypertension