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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
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
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
ClÃnic 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
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
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