000171048 001__ 171048
000171048 005__ 20260505142650.0
000171048 0247_ $$2doi$$a10.1007/s10439-026-04115-8
000171048 0248_ $$2sideral$$a149159
000171048 037__ $$aART-2026-149159
000171048 041__ $$aeng
000171048 100__ $$aRosales, Ricardo M.
000171048 245__ $$a3D Segmentation of Multi-contrast Cardiac Magnetic Resonances With Topological Correction and Synthetic Data Augmentation
000171048 260__ $$c2026
000171048 5060_ $$aAccess copy available to the general public$$fUnrestricted
000171048 5203_ $$aPurpose: Automatic segmentation of cardiac magnetic resonance (CMR) images improves the evaluation of heart structure and function, helping clinical diagnosis and the generation of in silico models. Recent advances have introduced synthetic augmentation (SA) using generative adversarial networks (GANs) and topological correction (TC) via persistent homology to enhance segmentation with convolutional neural networks (CNNs). However, their combined effectiveness remains unexplored. Here, we extend and systematically evaluate these techniques, individually and in combination, for the first time in the context of three-dimensional (3D) CMR segmentation across challenging multi-vendor, multi-center, multi-class, and multi-contrast data sets. Methods: Data involved anisotropic, topologically inconsistent cine and late gadolinium-enhanced (LGE) CMRs, and isotropic, topologically consistent ex vivo CMRs. Topological priors were defined in each data set from ground truth label (GTL) assessments, and TC was applied by retraining the baseline 3D CNN with a loss function accounting for topological discrepancies. For SA, deformed GTLs were used to generate synthetic images using trained 3D GANs. Results: Consistent segmentation improvements were observed for the ex vivo data in both overlap with GTLs and topological precision when applying TC and SA individually and in combination. Notably, an enhanced identification of the infarction was obtained when SA and TC were used in the LGE data. Overall, SA increased the predictions overlap with GTLs, while TC reduced the topological discrepancies across all data sets. Conclusion: TC and SA demonstrate strong potential for improving 3D CMR segmentation on complex, real-world data sets, especially when topologically consistent data are available for training.
000171048 536__ $$9info:eu-repo/grantAgreement/ES/DGA/LMP97_21$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/EC/H2020/638284/EU/Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface/MODELAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 638284-MODELAGE$$9info:eu-repo/grantAgreement/EC/H2020/874827/EU/Computational biomechanics and bioengineering 3D printing to develop a personalized regenerative biological ventricular assist device to provide lasting functional support to damaged hearts/BRAV3$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 874827-BRAV3$$9info:eu-repo/grantAgreement/ES/MCIN/PLEC2021-008127$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-1405560B-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130459B-I00
000171048 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000171048 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000171048 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000171048 700__ $$0(orcid)0000-0002-1960-407X$$aPueyo, Esther$$uUniversidad de Zaragoza
000171048 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000171048 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000171048 773__ $$g(2026), [18 pp.]$$pAnn. biomed. eng.$$tAnnals of Biomedical Engineering$$x0090-6964
000171048 8564_ $$s4750505$$uhttps://zaguan.unizar.es/record/171048/files/texto_completo.pdf$$yVersión publicada
000171048 8564_ $$s2318890$$uhttps://zaguan.unizar.es/record/171048/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000171048 909CO $$ooai:zaguan.unizar.es:171048$$particulos$$pdriver
000171048 951__ $$a2026-05-05-13:36:33
000171048 980__ $$aARTICLE