Multicenter aortic vessel tree extraction using deep learning

Bernhard Scharinger; Antonio Pepe; Yuan Jin; Christina Gsaxner; Jianning Li; Jan Egger

doi:10.1117/12.2656936

10 April 2023 Multicenter aortic vessel tree extraction using deep learning

Bernhard Scharinger, Antonio Pepe, Yuan Jin, Christina Gsaxner, Jianning Li, Jan Egger

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 124681F (2023) https://doi.org/10.1117/12.2656936
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manual labor, but, in recent years, progress in deep learning techniques made the automation of this process viable. For this purpose, we tested different deep learning networks to segment the aortic vessel tree from computed tomography angiography (CTA) scans with a deep neural network consisting of an encoder-decoder architecture with skip connections and an optional self-attention block. The networks were trained on a dataset of 56 CTA scans from three different sources and resulted in Dice score similarities between 0.043−0.897. Generally, the classical U-Nets performed better than the ones containing a self-attention block, indicating that they might diminish performance for AVT segmentation. The quality of the resulting segmentations was highly dependent on the CTA image quality, especially on the contrast between the aorta and the surrounding tissues. However, the trained deep neural network can segment CTA scans well with limited computational resources and training data.

Citation Download Citation

Bernhard Scharinger, Antonio Pepe, Yuan Jin, Christina Gsaxner, Jianning Li, and Jan Egger "Multicenter aortic vessel tree extraction using deep learning", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 124681F (10 April 2023); https://doi.org/10.1117/12.2656936

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