Work

Automated Detection and Segmentation of Ascending Aorta Dilation on a Non-ECG-Gated Chest CT Using Deep Learning

Year: 2025

Type: article

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.

Source: Diagnostics

DOI: 10.3390/diagnostics15182336

Authors: Fargana Aghayeva, Yusuf Abdi, Ahmad Uzair, Ayaz Aghayev

Institutions: Brigham and Women's Hospital, Mass General Brigham, Northeastern University, University of Doha for Science and Technology, Harvard University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137