Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection

European Radiology Experimental

Original Article

Peter Raivio¹  Marko Kangasniemi²  Eero Salli²  Tuomas Kaseva²  Risto Kesävuori²  Sauli Savolainen³ 

[1] Department of Cardiac Surgery, Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland;Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland;Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland;Department of Physics, University of Helsinki, Helsinki, Finland;

关键词: Computed tomography angiography;    Convolutional neural network;    Machine learning;    Neural networks (computer);    Aortic dissection;   

DOI  :  10.1186/s41747-023-00342-z

 received in 2022-11-07, accepted in 2023-04-01,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundGuidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.MethodsA total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively.Results2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively.ConclusionsBoth CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs.Relevance statementDeep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection.Key points• 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately.• Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs.• Deep learning can expedite the creation of ground truth segmentations.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

41116_2023_37_Article_IEq50.gif

41116_2023_37_Article_IEq45.gif

Fig. 1

41116_2023_37_Article_IEq23.gif

Fig. 2

Fig. 1

41116_2023_37_Article_IEq13.gif

Fig. 1