Related Experiment Video
Updated: Jan 16, 2026
Three-Dimensional Printing of a Complex Aortic Anomaly
Published on: November 1, 2018
Automated Detection and Segmentation of Ascending Aorta Dilation on a Non-ECG-Gated Chest CT Using Deep Learning
Fargana Aghayeva1, Yusuf Abdi2, Ahmad Uzair3
1Department of Radiology, Brigham and Women's Hospital, Mass General Brigham, Boston, MA 02115, USA.
Insights
A new deep learning model automatically detects ascending aortic dilation on chest CT scans. This AI tool aids in identifying risk for aortic dissection, improving patient outcomes and reducing radiologist workload.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Radiology
- Cardiovascular Imaging
Background:
- Ascending aortic dilation is a key risk factor for aortic dissection.
- Dilation often goes undetected in routine, non-ECG-gated chest CT scans.
- Automated detection methods are needed for opportunistic screening.
Purpose of the Study:
- Develop and evaluate a deep learning pipeline for automated ascending aortic segmentation.
- Utilize non-ECG-gated chest CT scans for analysis.
- Improve detection rates of ascending aortic dilation.
Main Methods:
- A two-stage deep learning pipeline was designed.
- Incorporated a convolutional neural network (CNN) for slice classification.
- Employed a U-Net model for aortic segmentation on 500 non-ECG-gated CT scans.
Main Results:
- Achieved high performance on a test set: Dice Similarity Coefficient (DSC) of 99.21% and Intersection over Union (IoU) of 98.45%.
- Focus-slice classification accuracy reached 98.18%.
- Outperformed traditional and prior CNN-based methods with higher overlap metrics and low computational cost.
Conclusions:
- A lightweight CNN+U-Net model enables accurate, automated ascending aortic segmentation.
- The pipeline can enhance diagnostic accuracy for aortic dissection risk.
- Facilitates opportunistic detection of ascending aortic dilation in routine CT imaging, reducing radiologist workload.
Abstract:
Background/Objectives: Ascending aortic (AA) dilation (diameter ≥ 4.0 cm) is a significant risk factor for aortic dissection, yet it often goes unnoticed in routine chest CT scans performed for other indications. This study aimed to develop and evaluate a deep learning pipeline for automated AA segmentation using non-ECG-gated chest CT scans. Methods: We designed a two-stage pipeline integrating a convolutional neural network (CNN) for focus-slice classification and a U-Net-based segmentation model to extract the aortic region. The model was trained and validated on a dataset of 500 non-ECG-gated chest CT scans, encompassing over 50,000 individual slices. Results: On the held-out test set (10%), the model achieved a Dice similarity coefficient (DSC) score of 99.21%, an Intersection over Union (IoU) of 98.45%, and a focus-slice classification accuracy of 98.18%. Compared with traditional rule-based and prior CNN-based methods, the proposed approach achieved markedly higher overlap metrics while maintaining low computational overhead. Conclusions: A lightweight CNN+U-Net deep learning model can enhance diagnostic accuracy, reduce radiologist workload, and enable opportunistic detection of AA dilation in routine chest CT imaging.
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
01:22Aortic Regurgitation II: Clinical Features and Diagnostic Tests