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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
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
Non-invasive detection of cardiovascular disorders from radiology scans
requires quantitative image analysis of the heart and its substructures. There
are well-established measurements that radiologists use for diseases assessment
such as ejection fraction, volume of four chambers, and myocardium mass. These
measurements are derived as outcomes of precise segmentation of the heart and
its substructures. The aim of this paper is to provide such measurements
through an accurate image segmentation algorithm that automatically delineates
seven substructures of the heart from MRI and/or CT scans. Our proposed method
is based on multi-planar deep convolutional neural networks (CNN) with an
adaptive fusion strategy where we automatically utilize complementary
information from different planes of the 3D scans for improved delineations.
For CT and MRI, we have separately designed three CNNs (the same architectural
configuration) for three planes, and have trained the networks from scratch for
voxel-wise labeling for the following cardiac structures: myocardium of left
ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA),
right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We
have evaluated the proposed method with 4-fold-cross validation on the
multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. The
precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for
CT and MR images, respectively. While a CT volume was segmented about 50
seconds, an MRI scan was segmented around 17 seconds with the GPUs/CUDA
implementation.Comment: The paper is accepted to STACOM 201
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN
Anatomical and biophysical modeling of left atrium (LA) and proximal
pulmonary veins (PPVs) is important for clinical management of several cardiac
diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
and PPVs through visualization. However, there is a strong need for an advanced
image segmentation method to be applied to cardiac MRI for quantitative
analysis of LA and PPVs. In this study, we address this unmet clinical need by
exploring a new deep learning-based segmentation strategy for quantification of
LA and PPVs with high accuracy and heightened efficiency. Our approach is based
on a multi-view convolutional neural network (CNN) with an adaptive fusion
strategy and a new loss function that allows fast and more accurate convergence
of the backpropagation based optimization. After training our network from
scratch by using more than 60K 2D MRI images (slices), we have evaluated our
segmentation strategy to the STACOM 2013 cardiac segmentation challenge
benchmark. Qualitative and quantitative evaluations, obtained from the
segmentation challenge, indicate that the proposed method achieved the
state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and
efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).Comment: The paper is accepted by MICCAI 2017 for publicatio
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
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
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