Publication: A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset
Authors
Haro Orenes, Salvador de ; Bernabé García, Gregorio ; García Carrasco, José Manuel ; González Férez, Pilar
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Publisher
De Gruyter
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DOI
https://doi.org/10.1515/jib-2024-0048
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info:eu-repo/semantics/article
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
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.
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Citation
Journal of Integrative Bioinformatics 2025
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