477 research outputs found
Recommended from our members
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
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography
In patients with obstructive coronary artery disease, the functional
significance of a coronary artery stenosis needs to be determined to guide
treatment. This is typically established through fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA). We present a
method for automatic and non-invasive detection of patients requiring ICA,
employing deep unsupervised analysis of complete coronary arteries in cardiac
CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187
patients, 137 of them underwent invasive FFR measurement in 192 different
coronary arteries. These FFR measurements served as a reference standard for
the functional significance of the coronary stenosis. The centerlines of the
coronary arteries were extracted and used to reconstruct straightened
multi-planar reformatted (MPR) volumes. To automatically identify arteries with
functionally significant stenosis that require ICA, each MPR volume was encoded
into a fixed number of encodings using two disjoint 3D and 1D convolutional
autoencoders performing spatial and sequential encodings, respectively.
Thereafter, these encodings were employed to classify arteries using a support
vector machine classifier. The detection of coronary arteries requiring
invasive evaluation, evaluated using repeated cross-validation experiments,
resulted in an area under the receiver operating characteristic curve of on the artery-level, and on the patient-level. The
results demonstrate the feasibility of automatic non-invasive detection of
patients that require ICA and possibly subsequent coronary artery intervention.
This could potentially reduce the number of patients that unnecessarily undergo
ICA.Comment: This work has been accepted to IEEE TMI for publicatio
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
- …