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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
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
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation:An open-access grand challenge
Knowledge of whole heart anatomy is a prerequisite for many clinical
applications. Whole heart segmentation (WHS), which delineates substructures of
the heart, can be very valuable for modeling and analysis of the anatomy and
functions of the heart. However, automating this segmentation can be arduous
due to the large variation of the heart shape, and different image qualities of
the clinical data. To achieve this goal, a set of training data is generally
needed for constructing priors or for training. In addition, it is difficult to
perform comparisons between different methods, largely due to differences in
the datasets and evaluation metrics used. This manuscript presents the
methodologies and evaluation results for the WHS algorithms selected from the
submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge,
in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional
cardiac images covering the whole heart, including 60 CT and 60 MRI volumes,
all acquired in clinical environments with manual delineation. Ten algorithms
for CT data and eleven algorithms for MRI data, submitted from twelve groups,
have been evaluated. The results show that many of the deep learning (DL) based
methods achieved high accuracy, even though the number of training datasets was
limited. A number of them also reported poor results in the blinded evaluation,
probably due to overfitting in their training. The conventional algorithms,
mainly based on multi-atlas segmentation, demonstrated robust and stable
performance, even though the accuracy is not as good as the best DL method in
CT segmentation. The challenge, including the provision of the annotated
training data and the blinded evaluation for submitted algorithms on the test
data, continues as an ongoing benchmarking resource via its homepage
(\url{www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/}).Comment: 14 pages, 7 figures, sumitted to Medical Image Analysi