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Updated: Oct 18, 2025
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
Dissected aorta segmentation using convolutional neural networks
Tianling Lyu1, Guanyu Yang1, Xingran Zhao1
1Laboratory of Imaging Science and Technology, Southeast University, Nanjing, China.
Insights
This study introduces a deep learning algorithm for segmenting dissected aortas in CT scans. The method accurately captures aortic structures, improving treatment planning for this critical cardiovascular condition.
Area of Science:
- Medical Imaging
- Cardiovascular Pathology
- Artificial Intelligence in Medicine
Background:
- Aortic dissection is a life-threatening condition characterized by a tear in the aorta's intimal layer.
- Accurate 3-D morphological understanding of the dissected aorta is crucial for effective treatment planning.
- Current segmentation methods require improvement for precise aortic dissection analysis.
Purpose of the Study:
- To develop a deep-learning-based algorithm for automatic segmentation of dissected aortas in computed tomography angiography (CTA) images.
- To enhance the accuracy and robustness of aortic dissection segmentation.
- To provide a tool for better understanding the 3-D morphology of dissected aortas.
Main Methods:
- A two-step deep learning approach utilizing 3-D and 2-D convolutional neural networks (CNNs).
- Initial 3-D CNN divides the aorta into anatomical portions, followed by 2-D CNNs (PSPnet-based) for detailed segmentation.
- Integration of an edge extraction branch to improve intimal flap segmentation accuracy.
Main Results:
- The proposed algorithm achieved an average Dice index exceeding 92%, demonstrating high segmentation performance.
- The combined 3-D and 2-D model approach outperformed 3-D or 2-D only models in accuracy and robustness.
- The edge extraction branch significantly improved the Dice index near aortic boundaries from 73.41% to 81.39%.
Conclusions:
- The developed deep learning algorithm effectively segments dissected aortas, accurately capturing complex structures.
- The method demonstrates high performance in identifying the intimal flaps while minimizing false positives.
- This tool aids in the precise morphological assessment of dissected aortas, supporting clinical decision-making.
Background And Objective:
Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed.
Method:
In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area.
Results:
The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%.
Conclusions:
The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps.
