Browsing by Subject "Left ventricular non-compaction diagnosis"

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    A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset
    (De Gruyter, 2025-06-04) Haro Orenes, Salvador de; Bernabé García, Gregorio; García Carrasco, José Manuel; González Férez, Pilar; Ingeniería y Tecnología de Computadores
    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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    Application of YOLOv8 and a model based on vision transformers and UNet for LVNC diagnosis: advantages and limitations
    (Springer, 2025-04-25) De Haro, Salvador; González Férez, Pilar; García, José M.; Bernabé García, Gregorio; Ingeniería y Tecnología de Computadores
    Hypertrabeculation or left ventricular non-compaction (LVNC) is a cardiac condition that has recently been recognized. While several methods exist for accurately measuring the trabeculae in the ventricle, there is still no consensus within the medical community regarding the optimal approach. In previous work, we introduced DL-LVTQ, a tool based on a UNet convolutional neural network designed to quantify the trabeculae in the left ventricle. In this paper, we present an expanded dataset that includes new patients affected by a cardiomyopathy known as Titin, necessitating the retraining of the models involved in our study on this updated dataset to accurately infer future patients with this condition. We also introduce ViTUNet, a hybrid architecture that aims to merge the benefits of UNet and Vision Transformers for precise segmentation of the left ventricle. Furthermore, we train a YOLOv8 model to detect the left ventricle and integrate it with the hybrid model to focus segmentation on a region of interest around the ventricle. Regarding the precision quality achieved by ViTUNet using YOLOv8, results are quite similar to those obtained by the DL-LVTQ tool, suggesting that the dataset is a limiting factor in our improvement. To substantiate this, we conduct a detailed analysis of the MRI slices in the current dataset. By identifying and removing problematic slices, results significantly improve. The introduction of a YOLOv8 model alongside a deep learning model presents a promising approach.
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    Expanding the deep-learning model to diagnosis LVNC: limitations and trade-offs
    (Tailor & Francis, 2024-02-11) Bernabé García, Gregorio; González Férez, Pilar; García Carrasco, José Manuel; Casas, Guillem; González Carrillo, Josefa; Ingeniería y Tecnología de Computadores
    Hyper-trabeculation or non-compaction in the left ventricle of the myocardium (LVNC) is a recently classified form of cardiomyopathy. Several methods have been proposed to quantify the trabeculae accurately in the left ventricle, but there is no general agreement in the medical community to use a particular approach. In the previous work, we proposed DL-LVTQ, a deep-learning approach for left ventricular trabecular quantification based on a U-Net CNN architecture. In this work, we have extended and adapted DL-LVTQ to cope with patients with different particularities and cardiomyopathies. Patient images were taken from different scanners and hospitals. We have modified and adapted the U-Net convolutional neural network to account for the different particularities of a heterogeneous group of patients with multiple cardiomyopathies and inherited cardiomyopathies. The inclusion of new groups of patients has increased the accuracy, specificity and Kappa values while maintaining the sensitivity of the proposed method. Therefore, a better-prepared diagnosis tool is ready for various cardiomyopathies with different characteristics. Cardiologists have considered that 98.9% of the evaluated outputs are verified clinically for diagnosis. Therefore, the high precision to segment the different cardiac structures allows us to make a robust diagnostic system bjective and faster, decreasing human error and time spent.
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    Improving a Deep Learning Model to Accurately Diagnose LVNC
    (Federico Guerra, 2023-12-12) Baron Yusti, Jaime Rafael; Bernabé García, Gregorio; González Férez, Pilar; García Carrasco, José Manuel; Casas, Guillem; González-Carrillo, Josefa; Ingeniería y Tecnología de Computadores
    Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult. We present techniques to improve segmentation and evaluate their impact on LVNC diagnosis. Three main methods are introduced: (1) using full 800 × 800 MR images rather than 512 × 512; (2) a clustering algorithm to eliminate neural network hallucinations; (3) advanced network architectures including Attention U-Net, MSA-UNet, and U-Net++.Experiments utilize cardiac MR datasets from three different hospitals. U-Net++ achieves the best segmentation performance using 800 × 800 images, and it improves the mean segmentation Dice score by 0.02 over the baseline U-Net, the clustering algorithm improves the mean Dice score by 0.06 on the images it affected, and the U-Net++ provides an additional 0.02 mean Dice score over the baseline U-Net. For LVNC diagnosis, U-Net++ achieves 0.896 accuracy, 0.907 precision, and 0.912 F1-score outperforming the baseline U-Net. Proposed techniques enhance LVNC detection, but differences between hospitals reveal problems in improving generalization. This work provides validated methods for precise LVNC diagnosis.