4,116 research outputs found
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans
acquired in screening enable quantification of atherosclerotic calcifications
and thus enable identification of subjects at increased cardiovascular risk.
This paper presents a method for automatic detection of coronary artery,
thoracic aorta and cardiac valve calcifications in low-dose chest CT using two
consecutive convolutional neural networks. The first network identifies and
labels potential calcifications according to their anatomical location and the
second network identifies true calcifications among the detected candidates.
This method was trained and evaluated on a set of 1744 CT scans from the
National Lung Screening Trial. To determine whether any reconstruction or only
images reconstructed with soft tissue filters can be used for calcification
detection, we evaluated the method on soft and medium/sharp filter
reconstructions separately. On soft filter reconstructions, the method achieved
F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta,
aortic valve and mitral valve calcifications, respectively. On sharp filter
reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively.
Linearly weighted kappa coefficients for risk category assignment based on per
subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter
reconstructions, respectively. These results demonstrate that the presented
method enables reliable automatic cardiovascular risk assessment in all
low-dose chest CT scans acquired for lung cancer screening
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
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The identification of pulmonary lobes is of great importance in disease
diagnosis and treatment. A few lung diseases have regional disorders at lobar
level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this
work, we propose an automated segmentation of pulmonary lobes using
coordination-guided deep neural networks from chest CT images. We first employ
an automated lung segmentation to extract the lung area from CT image, then
exploit volumetric convolutional neural network (V-net) for segmenting the
pulmonary lobes. To reduce the misclassification of different lobes, we
therefore adopt coordination-guided convolutional layers (CoordConvs) that
generate additional feature maps of the positional information of pulmonary
lobes. The proposed model is trained and evaluated on a few publicly available
datasets and has achieved the state-of-the-art accuracy with a mean Dice
coefficient index of 0.947 0.044.Comment: ISBI 2019 (Oral
Quantitative geometric analysis of rib, costal cartilage and sternum from childhood to teenagehood
Better understanding of the effects of growth on children’s bones and cartilage is necessary for clinical and biomechanical purposes. The aim of this study is to define the 3D geometry of children’s rib cages: including sternum, ribs and costal cartilage. Three-dimensional reconstructions of 960 ribs, 518 costal cartilages and 113 sternebrae were performed on thoracic CT-scans of 48 children, aged four months to 15 years. The geometry of the sternum was detailed and nine parameters were used to describe the ribs and rib cages. A "costal index" was defined as the ratio between cartilage length and whole rib length to evaluate the cartilage ratio for each rib level. For all children, the costal index decreased from rib level one to three and increased from level three to seven. For all levels, the cartilage accounted for 45 to 60% of the rib length, and was longer for the first years of life. The mean costal index decreased by 21% for subjects over three years old compared to those under three (p<10-4). The volume of the sternebrae was found to be highly age dependent. Such data could be useful to define the standard geometry of the paediatric thorax and help to detect clinical abnormalities.Grant from the ANR (SECUR_ENFANT 06_0385) and supported by the GDR 2610 “Biomécanique des chocs” (CNRS/INRETS/GIE PSA Renault
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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
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