Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI

Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net

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

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF

A Two-stage Method with a Shared 3D U-Net for Left Atrial Segmentation of Late Gadolinium-Enhanced MRI Images

Objective: This study was aimed at validating the accuracy of a proposed algorithm for fully automatic 3D left atrial segmentation and to compare its performance with existing deep learning algorithms. Methods: A two-stage method with a shared 3D U-Net was proposed to segment the 3D left atrium. In this architecture, the 3D U-Net was used to extract 3D features, a two-stage strategy was used to decrease segmentation error caused by the class imbalance problem, and the shared network was designed to decrease model complexity. Model performance was evaluated with the DICE score, Jaccard index and Hausdorff distance. Results: Algorithm development and evaluation were performed with a set of 100 late gadolinium-enhanced cardiovascular magnetic resonance images. Our method achieved a DICE score of 0.918, a Jaccard index of 0.848 and a Hausdorff distance of 1.211, thus, outperforming existing deep learning algorithms. The best performance of the proposed model (DICE: 0.851; Jaccard: 0.750; Hausdorff distance: 4.382) was also achieved on a publicly available 2013 image data set. Conclusion: The proposed two-stage method with a shared 3D U-Net is an efficient algorithm for fully automatic 3D left atrial segmentation. This study provides a solution for processing large datasets in resource-constrained applications. Significance Statement: Studying atrial structure directly is crucial for comprehending and managing atrial fibrillation (AF). Accurate reconstruction and measurement of atrial geometry for clinical purposes remains challenging, despite potential improvements in the visibility of AF-associated structures with late gadolinium-enhanced magnetic resonance imaging. This difficulty arises from the varying intensities caused by increased tissue enhancement and artifacts, as well as variability in image quality. Therefore, an efficient algorithm for fully automatic 3D left atrial segmentation is proposed in the present study

Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes

Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director/s: Gaspar Delso i Roser Sala. Tutor: Manel PuigAtrial fibrillation (AF) is the most prevalent type of arrhythmia nowadays. Even though it is associated with significant morbidity and mortality, there is still a substantial lack of basic understanding of the left atrium (LA) and pulmonary veins (PVs) anatomical structure that curbs the performance of current clinical treatments for the disease. Thus, segmentation and 3D reconstruction of the LA and PVs are of crucial importance for the diagnosis and treatment of AF. In this context, cardiac 3D Late Gadolinium Magnetic Resonance Imaging (LGE-MRI) appear as a very good tool for cardiac tissue characterization and myocardial fibrosis detection. In fact, these images have been proofed as reliable predictors of catheter ablation success, which is often the chosen treatment for AF patients. Several manual and semi-automatic segmentation tools from LGE-MRI scans are currently in use, but these are very time-consuming and highly prone to errors, hence the need for an automatic segmentation approach. With the rise of deep learning and convolutional neural networks, a number of automatic schemes are being developed. In this project, we evaluate a model that has been developed at the Hospital Clínic de Barcelona for obtaining an automatic segmentation of the LA using a deep learning architecture. Concretely, we tested this model with an independent set of images from another MRI vendor, and we obtained a set of quantitative and qualitative measures to validate the results. For the pursuit of our aims, this work begins with the state-of-the-art for LA segmentation of LGEMRI scans and with a market analysis of the field. We then present our proposed solution together with the obtained results and the corresponding conclusions

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patient