Publications
49 results found
Vimalesvaran K, Zaman S, Howard J, et al., 2024, Aortic stenosis assessment from the 3-chamber cine: ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity, Journal of Cardiovascular Magnetic Resonance, Vol: 26, ISSN: 1097-6647
Background:Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements.Methods:Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a ~1cm2 region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship.Results:314 patients (median age 69 [IQR 57–77], 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4–26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = -0.796, p=0.001), peak gradient (r = -0.772, p=0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of &
Martin-Isla C, Campello VM, Izquierdo C, et al., 2023, Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 27, Pages: 3302-3313, ISSN: 2168-2194
Zaman S, Vimalesvaran K, Howard JP, et al., 2023, Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI, Journal of Medical Artificial Intelligence, Vol: 6, ISSN: 2617-2496
BACKGROUND: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. METHODS: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). RESULTS: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's &kap
Varela M, Bharath AA, 2023, Prototype of a Cardiac MRI Simulator for the Training of Supervised Neural Networks, Pages: 366-374, ISBN: 9783031353017
Supervised deep learning methods typically rely on large datasets for training. Ethical and practical considerations usually make it difficult to access large amounts of healthcare data, such as medical images, with known task-specific ground truth. This hampers the development of adequate, unbiased and robust deep learning methods for clinical tasks. Magnetic Resonance Images (MRI) are the result of several complex physical and engineering processes and the generation of synthetic MR images provides a formidable challenge. Here, we present the first results of ongoing work to create a generator for large synthetic cardiac MR image datasets. As an application for the simulator, we show how the synthetic images can be used to help train a supervised neural network that estimates the volume of the left ventricular myocardium directly from cardiac MR images. Despite its current limitations, our generator may in the future help address the current shortage of labelled cardiac MRI needed for the development of supervised deep learning tools. It is likely to also find applications in the development of image reconstruction methods and tools to improve robustness, verification and interpretability of deep networks in this setting.
Lalande A, Chen Z, Pommier T, et al., 2022, Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge, MEDICAL IMAGE ANALYSIS, Vol: 79, ISSN: 1361-8415
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- Citations: 6
Varela M, Roy A, Lee J, 2022, A survey of pathways for mechano-electric coupling in the atria (vol 159, pg 136, 2021), PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, Vol: 169-170, Pages: 94-94, ISSN: 0079-6107
Bharath A, Uslu F, Varela Anjari M, et al., 2022, LA-Net: A multi-task deep network for the segmentation of the left atrium, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 456-464, ISSN: 0278-0062
Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.
Li Z, Petri C, Howard J, et al., 2022, PAT-CNN: automatic segmentation and quantification of pericardial adipose tissue from t2-weighted cardiac magnetic resonance images, Statistical Atlases and Computational Modeling of the Heart (STACOM), Publisher: Springer Nature Switzerland, Pages: 359-368, ISSN: 0302-9743
Background: Increased pericardial adipose tissue (PAT) is associated with many types of cardiovascular disease (CVD). Although cardiac magnetic resonance images (CMRI) are often acquired in patients with CVD, there are currently no tools to automatically identify and quantify PAT from CMRI. The aim of this study was to create a neural network to segment PAT from T2-weighted CMRI and explore the correlations between PAT volumes (PATV) and CVD outcomes and mortality.Methods: We trained and tested a deep learning model, PAT-CNN, to segment PAT on T2-weighted cardiac MR images. Using the segmentations from PAT-CNN, we automatically calculated PATV on images from 391 patients. We analysed correlations between PATV and CVD diagnosis and 1-year mortality post-imaging.Results: PAT-CNN was able to accurately segment PAT with Dice score/ Hausdorff distances of 0.74 ± 0.03/27.1 ± 10.9 mm, similar to the values obtained when comparing the segmentations of two independent human observers (0.76 ± 0.06/21.2 ± 10.3 mm). Regression models showed that, independently of sex and body-mass index, PATV is significantly positively correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value < 0.01).Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images automatically and accurately. Increased PATV as measured automatically from CMRI is significantly associated with the presence of CVD and can independently predict 1-year mortality.
Galazis C, Wu H, Li Z, et al., 2022, Tempera: spatial transformer feature pyramid network for cardiac MRI segmentation, 12th International Workshop, STACOM 2021, Publisher: Springer International Publishing, Pages: 268-276, ISSN: 0302-9743
Assessing the structure and function of the right ventricle (RV) is important in the diagnosis of several cardiac pathologies. However, it remains more challenging to segment the RV than the left ventricle (LV). In this paper, we focus on segmenting the RV in both short (SA) and long-axis (LA) cardiac MR images simultaneously. For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera). Our feature pyramid extends current designs by allowing not only a multi-scale feature output but multi-scale SA and LA input images as well. Tempera transfers learned features between SA and LA images via layer weight sharing and incorporates a geometric target transformer to map the predicted SA segmentation to LA space. Our model achieves an average Dice score of 0.836 and 0.798 for the SA and LA, respectively, and 26.31 mm and 31.19 mm Hausdorff distances. This opens up the potential for the incorporation of RV segmentation models into clinical workflows.
Herrero Martin C, Oved A, Chowdhury R, et al., 2021, EP-PINNs: cardiac electrophysiology characterisation using physics-informed neural networks, Frontiers in Cardiovascular Medicine, ISSN: 2297-055X
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation, but it is notoriously difficult to perform. We present EP-PINNs (Physics-Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation, from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.
Martin CH, Oved A, Chowdhury RA, et al., 2021, EP-PINNs: cardiac electrophysiology characterisation using physics-informed neural networks, Publisher: arXiv
Accurately inferring underlying electrophysiological (EP) tissue propertiesfrom action potential recordings is expected to be clinically useful in thediagnosis and treatment of arrhythmias such as atrial fibrillation, but it isnotoriously difficult to perform. We present EP-PINNs (Physics-Informed NeuralNetworks), a novel tool for accurate action potential simulation and EPparameter estimation, from sparse amounts of EP data. We demonstrate, using 1Dand 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporalevolution of action potentials, whilst predicting parameters related to actionpotential duration (APD), excitability and diffusion coefficients. EP-PINNs areadditionally able to identify heterogeneities in EP properties, making thempotentially useful for the detection of fibrosis and other localised pathologylinked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological invitro preparations, by characterising the effect of anti-arrhythmic drugs onAPD using optical mapping data. EP-PINNs are a promising clinical tool for thecharacterisation and potential treatment guidance of arrhythmias.
Lourenço A, Kerfoot E, Grigorescu I, et al., 2021, Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 12592 LNCS, Pages: 334-341, ISSN: 0302-9743
Delayed-enhancement cardiac magnetic resonance (DE-CMR) provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE) in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR. All the proposed networks achieved very good classification accuracy (>85%). Including information from DE-CMR (directly as images or as metadata following DE-CMR segmentation) is valuable in this classification task, improving the accuracy to 95–100%.
Tajabadi A, Roy A, Varela M, et al., 2021, Evolution of Epicardial Rotors into Breakthrough Waves during Atrial Fibrillation in 3D Canine Biatrial Model with Detailed Fibre Orientation, ISSN: 2325-8861
Atrial fibrillation (AF) is the most common arrhythmia, but its mechanisms are still unclear. Commonly observed phenomena during AF are epicardial re-entrant drivers (rotors) and breakthrough waves. This study aims to elucidate AF mechanisms, including links between rotors and breakthroughs. We used 3D canine atrial models based on micro-CT reconstruction of biatrial geometry combined with region-specific electrophysiology models. Hence, the 3D model included ionic and structural heterogeneities in the entire atria, with special focus on the right atrium (RA) and pectinate muscles (PM). Results were visualized through 3D atrial membrane voltage maps (VM), 2D isochronal maps (IM), and wave maps (WM). AF episodes were initiated in the atria and were maintained by several epicardial rotors in the PV and RA. Transmural rotors were also seen to propagate through the PM and reemerge at the RA epicardium during these episodes. IM and WM revealed multiple breakthroughs at the region where the PM connect to the RA. The VM simulations, as well as electrogram-based IM and WM, showed that the complex AF patterns seen experimentally can be explained by the interactions of epicardial and transmural rotors.
Varela Anjari M, Roy A, lee J, 2021, A survey of pathways for mechano-electric coupling in the atria, Progress in Biophysics and Molecular Biology, Vol: 159, Pages: 136-145, ISSN: 0079-6107
Mechano-electric coupling (MEC) in atrial tissue has received sparse investigation to date, despite the well-known association between chronic atrial dilation and atrial fibrillation (AF). Of note, no fewer than six different mechanisms pertaining to stretch-activated channels, cellular capacitance and geometric effects have been identified in the literature as potential players. In this mini review, we briefly survey each of these pathways to MEC. We then perform computational simulations using single cell and tissue models in presence of various stretch regimes and MEC pathways. This allows us to assess the relative significance of each pathway in determining action potential duration, conduction velocity and rotor stability. For chronic atrial stretch, we find that stretch-induced alterations in membrane capacitance decrease conduction velocity and increase action potential duration, in agreement with experimental findings. In the presence of time-dependent passive atrial stretch, stretch-activated channels play the largest role, leading to after-depolarizations and rotor hypermeandering. These findings suggest that physiological atrial stretches, such as passive stretch during the atrial reservoir phase, may play an important part in the mechanisms of atrial arrhythmogenesis.
Tajabadi A, Roy A, Varela M, et al., 2021, Evolution of Epicardial Rotors into Breakthrough Waves During Atrial Fibrillation in 3D Canine Biatrial Model with Detailed Fibre Orientation, Conference on Computing in Cardiology (CinC), Publisher: IEEE, ISSN: 2325-8861
Anjari M, Guha A, Burd C, et al., 2021, Apparent diffusion coefficient agreement and reliability using different region of interest methods for the evaluation of head and neck cancer post chemo-radiotherapy, DENTOMAXILLOFACIAL RADIOLOGY, Vol: 50, ISSN: 0250-832X
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- Citations: 5
Uslu F, Varela M, 2021, SA-NET: A SEQUENCE AWARE NETWORK FOR THE SEGMENTATION OF THE LEFT ATRIUM IN CINE MRI DATASETS, 18th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 766-769, ISSN: 1945-7928
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- Citations: 3
Roy A, Varela M, Chubb H, et al., 2020, Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
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- Citations: 13
Lourenço A, Kerfoot E, Dibblin C, et al., 2020, Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI, Publisher: arXiv
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia,characterised by a rapid and irregular electrical activation of the atria.Treatments for AF are often ineffective and few atrial biomarkers exist toautomatically characterise atrial function and aid in treatment selection forAF. Clinical metrics of left atrial (LA) function, such as ejection fraction(EF) and active atrial contraction ejection fraction (aEF), are promising, buthave until now typically relied on volume estimations extrapolated fromsingle-slice images. In this work, we study volumetric functional biomarkers ofthe LA using a fully automatic SEGmentation of the left Atrium based on aconvolutional neural Network (SEGANet). SEGANet was trained using a dedicateddata augmentation scheme to segment the LA, across all cardiac phases, in shortaxis dynamic (CINE) Magnetic Resonance Images (MRI) acquired with full cardiaccoverage. Using the automatic segmentations, we plotted volumetric time curvesfor the LA and estimated LA EF and aEF automatically. The proposed methodyields high quality segmentations that compare well with manual segmentations(Dice scores [$0.93 \pm 0.04$], median contour [$0.75 \pm 0.31$] mm andHausdorff distances [$4.59 \pm 2.06$] mm). LA EF and aEF are also in agreementwith literature values and are significantly higher in AF patients than inhealthy volunteers. Our work opens up the possibility of automaticallyestimating LA volumes and functional biomarkers from multi-slice CINE MRI,bypassing the limitations of current single-slice methods and improving thecharacterisation of atrial function in AF patients.
Varela Anjari M, Queiros S, Anjari M, et al., 2020, Strain maps of the left atrium imagedwith a novel high-resolutionCINEMRI protocol*, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology, Publisher: IEEE, Pages: 1178-1181, ISSN: 1557-170X
To date, regional atrial strains have not been imaged in vivo, despite their potential to provide useful clinical information. To address this gap, we present a novel CINE MRI protocol capable of imaging the entire left atrium at an isotropic 2-mm resolution in one single breath-hold.As proof of principle, we acquired data in 10 healthy volunteers and 2 cardiovascular patients using this technique.We also demonstrated how regional atrial strains can be estimated from this data following a manual segmentation of the left atrium using automatic image tracking techniques.The estimated principal strains vary smoothly across the left atrium and have a similar magnitude to estimates reported in the literature.
Uslu F, Varela M, Bharath AA, 2020, A semi-automatic method to segment the left atrium in MR volumes with varying slice numbers., 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Publisher: IEEE, Pages: 1198-1202
Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with dramatic increases in mortality and morbidity. Atrial cine MR images are increasingly used in the management of this condition, but there are few specific tools to aid in the segmentation of such data. Some characteristics of atrial cine MR (thick slices, variable number of slices in a volume) preclude the direct use of traditional segmentation tools. When combined with scarcity of labelled data and similarity of the intensity and texture of the left atrium (LA) to other cardiac structures, the segmentation of the LA in CINE MRI becomes a difficult task. To deal with these challenges, we propose a semi-automatic method to segment the left atrium (LA) in MR images, which requires an initial user click per volume. The manually given location information is used to generate a chamber location map to roughly locate the LA, which is then used as an input to a deep network with slightly over 0.5 million parameters. A tracking method is introduced to pass the location information across a volume and to remove unwanted structures in segmentation maps. According to the results of our experiments conducted in an in-house MRI dataset, the proposed method outperforms the U-Net [1] with a margin of 20 mm on Hausdorff distance and 0.17 on Dice score, with limited manual interaction.
Roy A, Varela M, Chubb H, et al., 2019, Virtual Catheter Ablation of Target Areas Identified from Image-Based Models of Atrial Fibrillation, 10th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 11-19, ISSN: 0302-9743
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- Citations: 1
Muffoletto M, Fu X, Roy A, et al., 2019, Development of a Deep Learning Method to Predict Optimal Ablation Patterns for Atrial Fibrillation, 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), Publisher: IEEE, Pages: 27-30
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- Citations: 1
Karim R, Blake L-E, Inoue J, et al., 2018, Algorithms for left atrial wall segmentation and thickness - Evaluation on an open-source CT and MRI image database, MEDICAL IMAGE ANALYSIS, Vol: 50, Pages: 36-53, ISSN: 1361-8415
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- Citations: 27
Roy A, Varela M, Aslanidi O, 2018, Image-Based Computational Evaluation of the Effects of Atrial Wall Thickness and Fibrosis on Re-entrant Drivers for Atrial Fibrillation, FRONTIERS IN PHYSIOLOGY, Vol: 9, ISSN: 1664-042X
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- Citations: 33
Varela M, Morgan R, Theron A, et al., 2017, Novel MRI Technique Enables Non-Invasive Measurement of Atrial Wall Thickness, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 1607-1614, ISSN: 0278-0062
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- Citations: 30
Varela M, Dar P, Hancox JC, et al., 2017, P921Small-conductance calcium-activated potassium current promotes atrial fibrillation due to reginal heterogeneity in the accumulation of intracellular calcium, EP Europace, Vol: 19, Pages: iii183-iii183, ISSN: 1099-5129
Varela M, Bisbal F, Zacur E, et al., 2017, Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation, FRONTIERS IN PHYSIOLOGY, Vol: 8, ISSN: 1664-042X
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- Citations: 43
Karim R, Varela M, Bhagirath P, et al., 2017, Segmentation challenge on the quantification of left atrial wall thickness, Pages: 193-200, ISSN: 0302-9743
This paper presents an image database for the Left Atrial Wall Thickness Quantification challenge at the MICCAI STACOM 2016 workshop along with some preliminary results. The image database consists of both CT (n = 10) and MRI (n = 10) datasets. Expert delineations from two observers were obtained for each image in the CT set and a single-observer segmentation was obtained for each image in the MRI set included in this study. Computer algorithms for segmentation of wall thickness from three research groups contributed to this challenge. The algorithms were evaluated on the basis of wall thickness measurements obtained from the segmentation masks.
Varela M, Dar P, Hancox JC, et al., 2017, The Efficacy of Class III Anti-arrhythmic Drugs in 3D Canine Atrial Models: Is the Blockade of I<sub>KCa</sub> Pro- or Anti-arrhythmic?, 44th Computing in Cardiology Conference (CinC), Publisher: IEEE COMPUTER SOC, ISSN: 2325-8861
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