Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

Autor:	Xu H; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK., Williams SE; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.; University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK., Williams MC; University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK., Newby DE; University/BHF Centre for Cardiovascular Science, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK., Taylor J; 3DLab, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield, s5 7AU, UK., Neji R; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.; MR Research Collaborations, Siemens Healthcare Limited, Newton House, Sir William Siemens Square, Frimley, Camberley, Surrey, GU16 8QD, UK., Kunze KP; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.; MR Research Collaborations, Siemens Healthcare Limited, Newton House, Sir William Siemens Square, Frimley, Camberley, Surrey, GU16 8QD, UK., Niederer SA; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK., Young AA; Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.
Jazyk:	angličtina
Zdroj:	European heart journal. Cardiovascular Imaging [Eur Heart J Cardiovasc Imaging] 2023 Apr 24; Vol. 24 (5), pp. 607-615.
DOI:	10.1093/ehjci/jead010
Abstrakt:	Aims: Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views. Methods and Results: A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n = 20). For the 200 test cases generated from CCTA, the network achieved a mean Dice score value of 93.7%, showing excellent 3D shape reconstruction from two views compared with the 3D segmentation Dice of 97.4%. The network also showed significantly lower mean absolute error values of 3.5 mL/4.9 cm2 for LA volume/surface area respectively compared to the area-length method errors of 13.0 mL/34.1 cm2 respectively (P < 0.05 for both). For the independent CMR test set, the network achieved accurate 3D shape estimation (mean Dice score value of 87.4%), and a mean absolute error values of 6.0 mL/5.7 cm2 for left atrial volume/surface area respectively, significantly less than the area-length method errors of 14.2 mL/19.3 cm2 respectively (P < 0.05 for both). Conclusions: Compared to the bi-plane area-length method, the network showed higher accuracy and robustness for both volume and surface area. Competing Interests: Conflict of interest: RN and KPK are employees of Siemens Healthcare Ltd. All others have no conflicts of interest to disclose. (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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