Browsing by Subject "Convolutional neural networks"

Now showing 1 - 4 of 4
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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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    Open Access
    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.
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    Open Access
    Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection
    (MDPI, 2025-01-06) Barón, Jaime R.; Bernabé García, Gregorio; González Férez, Pilar; García Carrasco, José M.; Casas, Guillem; González Carrillo, Josefa; Ingeniería y Tecnología de Computadores
    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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    Open Access
    Shadow detection using a cross-attentional dual-decoder network with self-supervised image reconstruction features
    (Elsevier, 2024-02-02) Fernández Beltrán, Rubén; Guzmán Ponce, Angélica; Fernandez, Rafael; Kang, Jian; García Mateos, Ginés; Informática y Sistemas; Facultad de Informática
    Shadow detection is a challenging problem in computer vision due to the high variability in lighting conditions, object shapes, and scene layouts. Despite the positive results achieved by some existing technologies, the problem becomes particularly challenging with complex and heterogeneous images where shadow-casting objects coexist and shadows can have different depths, scales, and morphologies. As a result, more advanced and accurate solutions are still needed to deal with this type of complexities. To address these challenges, this paper proposes a novel deep learning model, called the Cross-Attentional Dual Decoder Network (CADDN), to improve shadow detection by using fine-grained image reconstruction features. Unlike other existing methods, the CADDN uses an innovative encoder-decoder architecture with two decoder segments that work together to reconstruct the input images and their corresponding shadow masks. In this way, the features used to reconstruct the original input image can be used to support the shadow detection process itself. The proposed model also incorporates a cross-attention mechanism to weight the most relevant features for detecting shadows and skip connections with noise to improve the quality of the transferred features. The experimental results, including several benchmark image datasets and state-of-the-art detection methods, demonstrate the suitability of the presented approach for detecting shadows in computer vision applications.