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- 저자 : Giuseppe M. Raffa (검색결과 1 건)
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Deep learning approach for the segmentation of aneurysmal ascending aorta
Albert Comelli,Navdeep Dahiya,Alessandro Stefano,Viviana Benfante,Giovanni Gentile,Valentina Agnese,Giuseppe M. Raffa,Michele Pilato,Anthony Yezzi,Giovanni Petrucci,Salvatore Pasta 대한의용생체공학회 2021 Biomedical Engineering Letters (BMEL) Vol.11 No.1
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter,but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel imagederivedrisk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibilityand efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNettechniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspidaortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (MaterializeNV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance interms of accuracy and time inference were compared using several parameters. All deep learning models reported a dicescore higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that theENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deeplearning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating theexpansion into clinical workflow of personalized approach to the management of patients with ATAAs.
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