Deep learning-based cardiac computed tomography angiography left atrial segmentation and quantification in atrial fibrillation patients: a multi-model comparative study.
Authors
Affiliations (15)
Affiliations (15)
- Department of Cardiology of The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, China.
- State Key Laboratory of Transvascular Implantation Devices, Hangzhou, China.
- Cardiovascular Key Laboratory Zhejiang Province, Hangzhou, China.
- Transvascular Implant Instrument Research Institute, The Second Affiliated Hospital Zhejiang University School of Medicine, Binjiang District, Hang Zhou, 310053, China.
- ArteryFlow Technology Co., Ltd., Hangzhou, China.
- Department of Cardiology, Ningbo Medical Center Lihuili Hospital, Ningbo, China.
- Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Cardiology of The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, China. [email protected].
- State Key Laboratory of Transvascular Implantation Devices, Hangzhou, China. [email protected].
- Cardiovascular Key Laboratory Zhejiang Province, Hangzhou, China. [email protected].
- Transvascular Implant Instrument Research Institute, The Second Affiliated Hospital Zhejiang University School of Medicine, Binjiang District, Hang Zhou, 310053, China. [email protected].
- Department of Cardiology of The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, 310009, China. [email protected].
- State Key Laboratory of Transvascular Implantation Devices, Hangzhou, China. [email protected].
- Cardiovascular Key Laboratory Zhejiang Province, Hangzhou, China. [email protected].
- Transvascular Implant Instrument Research Institute, The Second Affiliated Hospital Zhejiang University School of Medicine, Binjiang District, Hang Zhou, 310053, China. [email protected].
Abstract
Quantitative assessment of left atrial volume (LAV) is an important factor in the study of the pathogenesis of atrial fibrillation. However, automated left atrial segmentation with quantitative assessment usually faces many challenges. The main objective of this study was to find the optimal left atrial segmentation model based on cardiac computed tomography angiography (CTA) and to perform quantitative LAV measurement. A multi-center left atrial study cohort containing 182 cardiac CTAs with atrial fibrillation was created, each case accompanied by expert image annotation by a cardiologist. Then, based on this left atrium dataset, five recent states-of-the-art (SOTA) models in the field of medical image segmentation were used to train and validate the left atrium segmentation model, including DAResUNet, nnFormer, xLSTM-UNet, UNETR, and VNet, respectively. Further, the optimal segmentation model was used to assess the consistency validation of the LAV. DAResUNet achieved the best performance in DSC (0.924 ± 0.023) and JI (0.859 ± 0.065) among all models, while VNet is the best performer in HD (12.457 ± 6.831) and ASD (1.034 ± 0.178). The Bland-Altman plot demonstrated the extremely strong agreement (mean bias - 5.69 mL, 95% LoA - 19-7.6 mL) between the model's automatic prediction and manual measurements. Deep learning models based on a study cohort of 182 CTA left atrial images were capable of achieving competitive results in left atrium segmentation. LAV assessment based on deep learning models may be useful for biomarkers of the onset of atrial fibrillation.