Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

© 2018 The Authors Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall\u27s thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n=10) and MRI (n=10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.Comment: 23 page

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods: The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results: Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions: The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface

A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ECG simulations

Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 23 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 104ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. The novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Statistiques de forme, de structure et de déformation à l'échelle d'une population pour l'étude de la fibrillation auriculaire

Atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by chaotic electrical activation and unsynchronized contraction of the atria. This epidemic and its life-threatening complications and fast progression call for diagnosis and effective treatment as early as possible. Catheter ablation, an invasive procedure that establishes lesions to block the trigger points of AF and creates a barrier to the propagation of the arrhythmia, is an effective treatment for patients refractory to anti-arrhythmic drugs. However, the success rate of the first-time ablation may range from 30% to 75%, such that multiple ablation procedures may be recommended, and atrial mechanical function may be adversely affected. With evolving imaging and digital technologies, the objective of the study is to understand the underlying physiology of AF better and to provide tools to assist clinical decision-making. We analyze the correlations between recurrent arrhythmia and patient characteristics before ablation, including the left atrial shape extracted from computed tomography images. Non-invasive extraction of the anatomical structures of the heart is a crucial prerequisite. We first developed semi-automatic methods to segment the left atrium and the left atrial wall from images. Next, we achieved good segmentation results with a neural network model. Then, we studied markers of shape related to both global and local remodeling, and the quantification of adipose tissue, deploying diffeomorphometry and statistical analysis tools. Finally, we extended the work to the statistical analysis of temporal deformation. We proposed a symmetric reformulation of the pole ladder, which improves the numerical consistency and stability.La fibrillation auriculaire (FA) est le type d'arythmie cardiaque la plus commun, caractérisée par une activation électrique chaotique et une contraction non synchronisée des oreillettes. Cette maladie et ses complications potentiellement mortelles ainsi que sa progression rapide exigent de diagnostiquer et de mettre en place un traitement efficace dès que possible. L'ablation par cathéter, une procédure invasive qui établit des lésions pour bloquer les points de déclenchement de la FA et la propagation de l'arythmie, est un traitement efficace pour les patients réfractaires aux médicaments. Cependant, pour 30% des patients, la FA se redéveloppe, entraînant des interventions d'ablation multiples et affectant la fonction mécanique auriculaire. Le but de cette étude est de combiner l'expertise mathématique et informatique à la médecine afin de mieux comprendre la physiologie sous-jacente à la FA et de fournir des outils d'aide à la décision aux cliniciens. Nous analysons des corrélations entre l'arythmie récurrente et les caractéristiques du patient avant l'ablation, y compris la forme de l’oreillette gauche extraite d'images tomodensitométriques. Nous développons pour ce faire des méthodes semi-automatiques pour segmenter l’oreillette gauche et sa paroi à partir d’images. Ensuite, nous avons obtenu de bons résultats de segmentation avec un modèle de réseau de neurones artificiels. En outre, nous étudions des marqueurs de forme liés au remodelage global et local, et la quantification du tissu adipeux, en combinant une approche morphométrique difféomorphe à une analyse statistique. Enfin, le travail s’étend à l’analyse statistique de la déformation temporelle. Nous proposons une reformulation symétrique de l'échelle de perroquet qui améliore la cohérence et la stabilité numérique