Publication: Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection
Authors
Barón, Jaime R. ; Bernabé García, Gregorio ; González Férez, Pilar ; García Carrasco, José M. ; Casas, Guillem ; González Carrillo, Josefa
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Publisher
MDPI
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DOI
https://doi.org/10.3390/jcm14010271
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info:eu-repo/semantics/article
Description
© 2025 by the authors. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published Manuscript version of a Published Work that appeared in final form in Journal of Clinical Medicine. To access the final edited and published work see https://doi.org/10.3390/jcm14010271
Abstract
Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. Methods: We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model. Results: Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the Trabecular Zone by up to 0.06 in certain populations. Conclusions: Our approach enhances the dataset’s quality, providing a more robust foundation for future LVNC diagnostic models.
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Citation
Journal of Clinical Medicine, 2025, Vol. 14 (1) : 271
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/