A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset

Haro Orenes, Salvador de; Bernabé García, Gregorio; García Carrasco, José Manuel; González Férez, Pilar

Publication:
A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset

dc.contributor	Bernabé García, Gregorio
dc.contributor.author	Haro Orenes, Salvador de
dc.contributor.author	Bernabé García, Gregorio
dc.contributor.author	García Carrasco, José Manuel
dc.contributor.author	González Férez, Pilar
dc.contributor.department	Ingeniería y Tecnología de Computadores	es
dc.date.accessioned	2025-06-23T08:09:33Z
dc.date.available	2025-06-23T08:09:33Z
dc.date.issued	2025-06-04
dc.description	© 2025 the author(s). 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 version of a Published Work that appeared in final form in Journal of Integrative Bioinformatics . To access the final edited and published work see https://doi.org/10.1515/jib-2024-0048
dc.description.abstract	Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle’s inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.
dc.format	application/pdf	es
dc.format.extent	16	es
dc.identifier.citation	Journal of Integrative Bioinformatics 2025
dc.identifier.doi	https://doi.org/10.1515/jib-2024-0048
dc.identifier.issn	Electronic: 1613-4516
dc.identifier.uri	http://hdl.handle.net/10201/156440
dc.language	eng	es
dc.publisher	De Gruyter
dc.relation	This work has been partially funded by Grant TED2021-129221B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. This work has been carried out in collaboration with the Hospitals Virgen of Arrixaca and Vall d’Hbron in Murcia and Barcelona (Spain).	es
dc.relation.publisherversion	https://www.degruyterbrill.com/document/doi/10.1515/jib-2024-0048/html
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Data analysis
dc.subject	Image detection
dc.subject	Left ventricular non-compaction diagnosis
dc.subject	Medical imaging
dc.subject	Convolutional neural networks
dc.title	A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es