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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
Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge
This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the heart using a priori information. From a fast marching initialisation, two fronts representing the endocardium and epicardium boundaries of the left ventricle are evolved as the zero level-set of a higher dimension function. We introduce a novel and robust stopping term using both gradient and region-based information. The segmentation is supervised both with a coupling function and using a probabilistic model built from training instances. The robustness of the segmentation scheme is evaluated by performing a segmentation on four unseen data-sets containing high variation and the performance of the segmentation is quantitatively assessed
Automatic segmentation of the left ventricle cavity and myocardium in MRI data
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method
based on a novel deep convolutional neural network (CNN) designed for the 2017
ACDC MICCAI challenge. The novelty of our network comes with its embedded shape
prior and its loss function tailored to the cardiac anatomy. Our model includes
a cardiac centerof-mass regression module which allows for an automatic shape
prior registration. Also, since our method processes raw MR images without any
manual preprocessing and/or image cropping, our CNN learns both high-level
features (useful to distinguish the heart from other organs with a similar
shape) and low-level features (useful to get accurate segmentation results).
Those features are learned with a multi-resolution conv-deconv "grid"
architecture which can be seen as an extension of the U-Net. Experimental
results reveal that our method can segment the left and right ventricles as
well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an
average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.Comment: 8 pages, 1 tables, 2 figure
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions
This work is dedicated to the simulation of full cycles of the electrical
activity of the heart and the corresponding body surface potential. The model
is based on a realistic torso and heart anatomy, including ventricles and
atria. One of the specificities of our approach is to model the atria as a
surface, which is the kind of data typically provided by medical imaging for
thin volumes. The bidomain equations are considered in their usual formulation
in the ventricles, and in a surface formulation on the atria. Two ionic models
are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal
model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and
Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin
boundary condition based on a resistor- capacitor transmission condition.
Various ECGs are simulated in healthy and pathological conditions (left and
right bundle branch blocks, Bachmann's bundle block, Wolff-Parkinson-White
syndrome). To assess the numerical ECGs, we use several qualitative and
quantitative criteria found in the medical literature. Our simulator can also
be used to generate the signals measured by a vest of electrodes. This
capability is illustrated at the end of the article
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