468 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
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation
Analysis and modeling of the ventricles and myocardium are important in the
diagnostic and treatment of heart diseases. Manual delineation of those tissues
in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the
boundaries makes the segmentation task rather challenging. Furthermore, the
annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,
are often not available. We propose an end-to-end segmentation framework based
on convolutional neural network (CNN) and adversarial learning. A dilated
residual U-shape network is used as a segmentor to generate the prediction
mask; meanwhile, a CNN is utilized as a discriminator model to judge the
segmentation quality. To leverage the available annotations across modalities
per patient, a new loss function named weak domain-transfer loss is introduced
to the pipeline. The proposed model is evaluated on the public dataset released
by the challenge organizer in MICCAI 2019, which consists of 45 sets of
multi-sequence CMR images. We demonstrate that the proposed adversarial
pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc
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
- …