Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.

Autor: Zhang X; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA., Cerna AEU; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA., Stough JV; Computer Science, Bucknell University, Lewisburg, PA, USA., Chen Y; Computer Science, Bucknell University, Lewisburg, PA, USA., Carry BJ; Heart Institute, Geisinger, Danville, PA, USA., Alsaid A; Heart Institute, Geisinger, Danville, PA, USA., Raghunath S; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA., vanMaanen DP; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA., Fornwalt BK; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA.; Heart Institute, Geisinger, Danville, PA, USA.; Department of Radiology, Geisinger, Danville, PA, USA., Haggerty CM; Department of Translational Data Science and Informatics, Geisinger, 100 North Academy Avenue, Danville, PA, 17822, USA. chris.m.haggerty@gmail.com.; Heart Institute, Geisinger, Danville, PA, USA. chris.m.haggerty@gmail.com.
Jazyk: angličtina
Zdroj: The international journal of cardiovascular imaging [Int J Cardiovasc Imaging] 2022 Aug; Vol. 38 (8), pp. 1685-1697. Date of Electronic Publication: 2022 Feb 24.
DOI: 10.1007/s10554-022-02554-7
Abstrakt: Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the tenfold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty. We observed that removing segmentations with high uncertainty from 14 to 71% studies reduced volume/mass MAE by 6-10%. The addition of convexity filters improved specificity, efficiently removing < 10% studies with large MAE (16-40%). In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset-segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses-with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.
(© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)
Databáze: MEDLINE
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