Deep neural network architectures for cardiac image segmentation

El-Taraboulsi, Jasmine; Cabrera, Claudia P.; Roney, Caroline; Aung, Nay

doi:10.1016/j.ailsci.2023.100083

Cited by 12 publications

(4 citation statements)

References 69 publications

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“…New methods have been developed for cardiac segmentation in recent years, e.g. the usage of transformers within neural networks for segmentation is a more recent idea than the use of convolutional neural networks and might be an essential element for future research 30 . A combination of nnU-Net with transformers 31 shows promising results, especially in an ensemble with the unmodified nnU-Net.…”

Section: Discussionmentioning

confidence: 99%

“…A combination of nnU-Net with transformers 31 shows promising results, especially in an ensemble with the unmodified nnU-Net. However, the highest performance of deep learning neural networks for segmentation in the field of CMR is still achieved by convolutional neural networks, often based on the U-Net or the nnU-Net 30 . Therefore, it seems reasonable to have nnU-Net as the state-of-the-art method, in particular because of its accessibility through pre-trained weights.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

Schilling,

Unterberg-Buchwald,

Lotz

et al. 2024

Sci Rep

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In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice’s coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice’s coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice’s coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice’s coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

Schilling,

Unterberg-Buchwald,

Lotz

et al. 2024

Sci Rep

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“…Another practical usage of DRNN is image segmentation (for example, cardiac). In [6] authors reviewed 60 works by using deep learning methods in cardiac image segmentation. They did comparative analysis of different network architectures with accuracies.…”

Section: Introduction (Literature Review)mentioning

confidence: 99%

Deep Recurrent Neural Networks in Energy Demand Forecasting: A Case Study of Kazakhstan's Electrical Consumption

Kabdygali,

Omirgaliyev,

Tursynbayev

et al. 2024

sjaitu

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The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.

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“…New automatic methods based on DNNs have been developed to determine the left ventricle volume through Magnetic Resonance Imaging (MRI) [16,17]. Recent publications also exploit various possibilities of deep learning to segment the left and the right ventricle by using highperformance computing [18][19][20][21][22]. In particular, DL-LVTQ is a new automatic proposal based on a U-Net architecture [23] to diagnose LVNC.…”

Section: Introductionmentioning

confidence: 99%

Improving a Deep Learning Model to Accurately Diagnose LVNC

Barón,

Bernabé,

González-Férez

et al. 2023

JCM

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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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Deep neural network architectures for cardiac image segmentation

Cited by 12 publications

References 69 publications

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

Deep Recurrent Neural Networks in Energy Demand Forecasting: A Case Study of Kazakhstan's Electrical Consumption

Improving a Deep Learning Model to Accurately Diagnose LVNC

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