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Three-Dimensional Printing of a Complex Aortic Anomaly
Published on: November 1, 2018
Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques
Christos Mavridis1, Theodore L Economopoulos2, Georgios Benetos3
1Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Athens, Greece. chmavridis@biomed.ntua.gr.
Insights
This study introduces a new automatic 3D segmentation method for aorta detection in CT scans. The novel approach significantly improves accuracy for clinical diagnosis and treatment planning.
Area of Science:
- Medical Imaging Analysis
- Computational Anatomy
- Artificial Intelligence in Medicine
Background:
- Aorta segmentation is critical for diagnosing pathologies like dissections and aneurysms.
- Accurate segmentation aids in risk assessment and complication prediction, saving lives.
- Current methods often require manual intervention, which is time-consuming.
Purpose of the Study:
- To present a novel, fully automatic 3D segmentation method for aorta detection in CT imaging.
- To combine image processing and machine learning for robust aorta modeling.
- To improve the efficiency and accuracy of aorta segmentation in clinical settings.
Main Methods:
- A two-stage segmentation process was developed.
- Initial segmentation used intensity thresholding.
- Subsequent classification employed a Markov Random Field network.
Main Results:
- The method was validated on 16 3D CT datasets.
- 3D models of the aorta were successfully reconstructed.
- Quantitative and qualitative evaluations demonstrated superior accuracy compared to existing techniques.
Conclusions:
- The proposed method achieves superior segmentation performance and accuracy.
- This automated scheme can accelerate medical imaging data evaluation.
- It holds significant potential for clinical applications like treatment planning and assessment.
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.
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