Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Jamart, Kevin; Xiong, Zhaohan; Talou, Gonzalo D. Maso; Stiles, Martin K.; Zhao, Jichao

doi:10.3389/fcvm.2020.00086

Cited by 33 publications

(29 citation statements)

References 82 publications

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“…There are a few challenges recently organized to benchmark the new methodologies proposed for the cardiac anatomy segmentation -2018 LA Segmentation Challenge in MICCAI 2018 (LASC'18) (Xiong et al, 2021) for LA, MS-CMR (MS-CMR Challenge, 2019; Pop et al, 2020) in MICCAI 2019, and MyoPS 2020(MyoPS Challenge, 2020Zhuang and Li, 2020) in MICCAI 2020 for LV. With the recent development in deep learning, we can observe a range of methodologies developed for LA and LV segmentation in LGE CMR (Jamart et al, 2020).…”

Section: Why Use Deep Learning In the Anatomical Structure Segmentation?mentioning

confidence: 99%

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Tang

et al. 2021

Front. Physiol.

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Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

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Section: Why Use Deep Learning In the Anatomical Structure Segmentation?mentioning

confidence: 99%

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Tang

et al. 2021

Front. Physiol.

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“…Based on experimental/clinical data on medical imaging and invasively acquired electroanatomic maps, atrial geometry with wall thickness [ 102 ], fibrosis distribution [ 103 , 104 ], myofibre orientation, regional electrical heterogeneities and AF driver distribution [ 105 ] were used to develop patient-specific 3D models [ 106 ]. In details, models with real atrial geometry are reconstructed from medical imaging, specifically from cardiac MRI and/or cardiac CT scans using image segmentation and 3D reconstruction algorithms [ 107 , 108 , 109 , 110 ]. In the 3D atria, fibrosis can be detected on late gadolinium enhancement MRI (LGE-MRI) using different thresholding techniques [ 111 , 112 ].…”

Section: Recent Advances In Atrial Modellingmentioning

confidence: 99%

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

Bai

Zhu

et al. 2021

IJMS

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Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

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“…Furthermore, multi-atlas-based segmentation and, more recently, ML algorithms such as support vector machines as statistical classifiers, have also gained interest in the field of cardiac image segmentation and classification. Despite the promising results shown by both the non-ML and ML methods mentioned above, their ad hoc nature and reliability on good initialization have limited their widespread adoption in clinical practice [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Saiz-Vivó

Colomer

Fonfria

et al. 2021

Entropy

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Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

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Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Cited by 33 publications

References 82 publications

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

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