Quantitative analysis of 3D thoracic aortic aneurysm on chest x-rays using conditional generative adversarial network

Samuel Cho; Jong-Min Kim; Sang Joon Park

doi:10.1117/12.3005068

3 April 2024 Quantitative analysis of 3D thoracic aortic aneurysm on chest x-rays using conditional generative adversarial network

Samuel Cho, Jong-Min Kim, Sang Joon Park

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129261R (2024) https://doi.org/10.1117/12.3005068
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

The lack of general screening for thoracic aortic aneurysm (TAA) compared to abdominal aortic aneurysm is in part due to the higher costs of CT scan or MRI compared to abdominal ultrasound. Here, we introduce a deep learning method to easily detect TAA from a less expensive imaging modality: a single chest x-ray. We use a two-step approach. First, we use image-to-image translation using conditional generative adversarial network to separate the tissue layers (e.g. bone, lung, ascending/descending aorta). Next, we apply a skeletonization algorithm to compute the diameters of the ascending and descending aorta. We validate this method on a paired CXR and CT dataset and find high agreement between the computed diameter from the CXR and the true diameter from the CT scan. The mean absolute error for all cross section diameters and maximum diameters is 3.58mm and 1.97mm, respectively. In detecting dilation (>35mm), our method achieves an F1 score of 0.87 and Cohen's kappa of 0.75, showing high agreement.

Citation Download Citation

Samuel Cho, Jong-Min Kim, and Sang Joon Park "Quantitative analysis of 3D thoracic aortic aneurysm on chest x-rays using conditional generative adversarial network", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261R (3 April 2024); https://doi.org/10.1117/12.3005068

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