The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and randomly split in a training (n = 55 patients), validation (n = 8 patients), and a test set (n = 8 patients). A fully automatic deep-learning method combining two convolutional neural networks performed segmentation of 10 cardiovascular structures, which was compared with the manually segmented reference by the Dice index. Correlations and agreement between myocardial volumes and mass were assessed. The algorithm demonstrated high accuracy (Dice score = 0.920; interquartile range: 0.906–0.925) and a low computing time (13.4 s, range 11.9–14.9). Correlations and agreement of volumes and mass were satisfactory for most structures. Six of ten structures were well segmented. Deep-learning-based method allowed automated WHS from ECG-gated CT data with a high accuracy. Challenges remain to improve right-sided structures segmentation and achieve daily clinical application.
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Whole heart segmentation
Transcatheter aortic valve implantation
Left ventricular cavity
Left ventricular myocardium
Superior vena cava
Right ventricular cavity
Region of interest
Convolutional neural network
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Conflict of Interest
Dr. Vincent Auffret and Dr. Le Breton received lecture fees from Edwards Lifescience. Mr. Florent Lalys is one of the employees of Therenva®. The other authors have nothing to disclose. All the authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
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Sharobeem, S., Le Breton, H., Lalys, F. et al. Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach. J. of Cardiovasc. Trans. Res. (2021). https://doi.org/10.1007/s12265-021-10166-0
- Whole heart segmentation
- Deep learning
- Computed tomography
- Procedural planning