Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network

Razeghi, Orod; Sim, Iain; Roney, Caroline H.; Karim, Rashed; Chubb, Henry; Whitaker, John; O’Neill, Louisa; Mukherjee, Rahul; Wright, Matthew; O’Neill, Mark; Williams, Steven E.; Niederer, Steven

doi:10.1161/circimaging.120.011512

Cited by 21 publications

(35 citation statements)

References 32 publications

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“…The increasing availability of public cross-sectional imaging datasets now facilitates the intra-group comparison of techniques for image segmentation or registration across large datasets from different centres and scanner vendors. 101 Contributing to reproducibility, there are several open-source software platforms (for example, CemrgApp 37 and OpenEP 102 ) for processing imaging and electroanatomic data sets. Releasing codes and trained networks to the community will advance the field and enable reproducible operator-independent analyses.…”

Section: Section 3: Using Advanced Imaging Techniques To Guide Ablation Proceduresmentioning

confidence: 99%

Applications of multimodality imaging for left atrial catheter ablation

Roney

Sillett

Whitaker

et al. 2021

European Heart Journal - Cardiovascular Imaging

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Atrial arrhythmias, including atrial fibrillation and atrial flutter, may be treated through catheter ablation. The process of atrial arrhythmia catheter ablation, which includes patient selection, pre-procedural planning, intra-procedural guidance, and post-procedural assessment, is typically characterized by the use of several imaging modalities to sequentially inform key clinical decisions. Increasingly, advanced imaging modalities are processed via specialized image analysis techniques and combined with intra-procedural electrical measurements to inform treatment approaches. Here, we review the use of multimodality imaging for left atrial ablation procedures. The article first outlines how imaging modalities are routinely used in the peri-ablation period. We then describe how advanced imaging techniques may inform patient selection for ablation and ablation targets themselves. Ongoing research directions for improving catheter ablation outcomes by using imaging combined with advanced analyses for personalization of ablation targets are discussed, together with approaches for their integration in the standard clinical environment. Finally, we describe future research areas with the potential to improve catheter ablation outcomes.

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Section: Section 3: Using Advanced Imaging Techniques To Guide Ablation Proceduresmentioning

confidence: 99%

Applications of multimodality imaging for left atrial catheter ablation

Roney

Sillett

Whitaker

et al. 2021

European Heart Journal - Cardiovascular Imaging

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“…between random variables x 1 and x 2 is not specified or constrained by equation (1). In order to obtain paired samples, according to [26], we extend the conditional entropies from single constraint to bi-direction constraints (H(x 1 |x 2 ) and H(x 2 |x 1 )), which imposes constraints on the conditionals p ϕ1 (x 2 |x 1 ) and q ϕ2 (x 1 |x 2 ), simultaneously.…”

Section: A Overviewmentioning

confidence: 99%

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

Zhang¹,

Mohiaddin²,

Wong³

et al. 2021

Preprint

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Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semisupervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dualmodelling networks for mining the complementary information in both intra-domain and inter-domain. For the intradomain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dualmodelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from

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“…Beyond anatomy, image analysis to delineate scar and BZ from viable healthy myocardium is even more important. Validation and optimization of segmentation approaches are also rapidly evolving for both CMR Karim et al (2013); Razeghi et al (2020) and CT Yamashita et al (2016); Cedilnik et al (2018), which will facilitate, the robust and accurate model generation from higher resolution clinical images.…”

Section: Clinical Imaging and Model Constructionmentioning

confidence: 99%

An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias

Campos¹,

Neic²,

Costa³

et al. 2021

Preprint

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Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, stateof-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.

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Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network

Cited by 21 publications

References 32 publications

Applications of multimodality imaging for left atrial catheter ablation

Applications of multimodality imaging for left atrial catheter ablation

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias

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