MA-SOCRATIS: An automatic pipeline for robust segmentation of the left ventricle and scar

Mamalakis, Michail; Garg, Pankaj; Nelson, Tom; Lee, Justin; Wild, Jim M.; Clayton, Richard H.

doi:10.1016/j.compmedimag.2021.101982

Cited by 8 publications

(5 citation statements)

References 66 publications

(107 reference statements)

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“…The proposed method provided explanation for obtaining results in the form of segmentation mask without the need of labels for training. Furthermore, an automatic pipelines for myocardial and scar segmentation from short axis LGE-MRI was proposed by Mamalakis et al [36]. The initial segmentation step is to estimate the myocardial boundaries by applying multi-atlas segmentation techniques.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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“…As a result, these algorithms frequently suffer low accuracy and robustness. Other methods are model-based methods like atlas models, which need a large dataset to capture the geometrical shape distribution of the heart for sufficient generalization [ 64 ]. Figure 3 [ 51 , 65 ] illustrates the role of ML in left ventricular segmentation techniques used in CMR.…”

Section: Cardiac Mrimentioning

confidence: 99%

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

et al. 2023

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Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

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“…One main challenge in patient-specific modeling for the left heart is the dynamic nature of the geometry Γ LV . With the advance of machine learning algorithms, various groups [139] have proposed an automated process for delineating the LV geometrical deformation for hemodynamic simulations [140]. In addition, recent developments in ultrasound techniques [70,141] and 4D CT scans [142] can provide higher temporal and spatial resolutions for Γ LV dynamics as well as the intraventricular flows.…”

Section: Current Challenges and Future Directionsmentioning

confidence: 99%

Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies

Usta

Aidun

et al. 2022

Fluids

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Given the complexity of human left heart anatomy and valvular structures, the fluid–structure interaction (FSI) simulation of native and prosthetic valves poses a significant challenge for numerical methods. In this review, recent numerical advancements for both fluid and structural solvers for heart valves in patient-specific left hearts are systematically considered, emphasizing the numerical treatments of blood flow and valve surfaces, which are the most critical aspects for accurate simulations. Numerical methods for hemodynamics are considered under both the continuum and discrete (particle) approaches. The numerical treatments for the structural dynamics of aortic/mitral valves and FSI coupling methods between the solid Ωs and fluid domain Ωf are also reviewed. Future work toward more advanced patient-specific simulations is also discussed, including the fusion of high-fidelity simulation within vivo measurements and physics-based digital twining based on data analytics and machine learning techniques.

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MA-SOCRATIS: An automatic pipeline for robust segmentation of the left ventricle and scar

Cited by 8 publications

References 66 publications

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies

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