Fully automated multi-heartbeat echocardiography video segmentation and motion tracking

Yida Chen; Xiaoyan Zhang; Christopher M. Haggerty; Joshua V. Stough

doi:10.1117/12.2607871

4 April 2022 Fully automated multi-heartbeat echocardiography video segmentation and motion tracking

Yida Chen, Xiaoyan Zhang, Christopher M. Haggerty, Joshua V. Stough

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120320R (2022) https://doi.org/10.1117/12.2607871
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Neural network-based video segmentation has proven effective in producing temporally-coherent segmentation and motion tracking of heart substructures in echocardiography. However, prior methods confine analysis to half-heartbeat systolic phase clips from end-diastole (ED) to end-systole (ES), requiring the specification of these frames in the video and limiting clinical applicability. Here we introduce CLAS-FV, a fully automated framework that extends upon this prior work, providing joint semantic segmentation and motion tracking in multi-beat echocardiograms. Our framework first employs a modified R2+1D ResNet stem, which is efficient in encoding spatiotemporal features, and further leverages sliding windows for both training and test time augmentation to accommodate the full cardiac cycle. First, through 10-fold cross-validation on the half-beat CAMUS dataset, we show that the R2+1D-based stem outperforms the prior 3D U-Net both in Dice overlap for all substructures, and in derived clinical indices of ED and ES ventricular volumes and ejection fraction (EF). Next, we use the large clinical EchoNet-Dynamic dataset to extend our framework to full multi-beat video segmentation. We obtain mean Dice overlap of 0.94/0.91 on left ventricle endocardium in ED/ES phases, and accurately infer EF (mean absolute error 5.3%) over 1269 test patients. The presented multi-heartbeat video segmentation framework promises fast and coherent segmentation and motion tracking for the rich phenotypic analysis of echocardiography.

Conference Presentation

Citation Download Citation

Yida Chen, Xiaoyan Zhang, Christopher M. Haggerty, and Joshua V. Stough "Fully automated multi-heartbeat echocardiography video segmentation and motion tracking", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120320R (4 April 2022); https://doi.org/10.1117/12.2607871

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