Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

Autor:	Priya S; Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa (S.P.). Electronic address: sarv-priya@uiowa.edu., Dhruba DD; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.)., Perry SS; Department of Biostatistics, University of Iowa, Iowa City, Iowa (S.S.P.)., Aher PY; Department of Radiology, University of Miami, Miller School of Medicine, Miami, Florida (P.Y.A.)., Gupta A; Department of Radiology, University Hospital Cleveland Medical Center, Cleveland, Ohio (A.G.)., Nagpal P; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin (P.N.)., Jacob M; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.).
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Feb; Vol. 31 (2), pp. 503-513. Date of Electronic Publication: 2023 Aug 03.
DOI:	10.1016/j.acra.2023.07.008
Abstrakt:	Rationale and Objectives: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. Materials and Methods: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). Results: The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. Conclusion: Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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