327,959 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
Symmetric angular momentum coupling, the quantum volume operator and the 7-spin network: a computational perspective
A unified vision of the symmetric coupling of angular momenta and of the
quantum mechanical volume operator is illustrated. The focus is on the quantum
mechanical angular momentum theory of Wigner's 6j symbols and on the volume
operator of the symmetric coupling in spin network approaches: here, crucial to
our presentation are an appreciation of the role of the Racah sum rule and the
simplification arising from the use of Regge symmetry. The projective geometry
approach permits the introduction of a symmetric representation of a network of
seven spins or angular momenta. Results of extensive computational
investigations are summarized, presented and briefly discussed.Comment: 15 pages, 10 figures, presented at ICCSA 2014, 14th International
Conference on Computational Science and Application
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segmentation is therefore highly desirable, but difficult due to the
proximity of the lesions to bony structures with a similar attenuation
coefficient. In this paper, we propose a method for automatic segmentation of
ICAC; the first to our knowledge. Our method is based on a 3D fully
convolutional neural network that we extend with two regularization techniques.
Firstly, we use deep supervision (hidden layers supervision) to encourage
discriminative features in the hidden layers. Secondly, we augment the network
with skip connections, as in the recently developed ResNet, and dropout layers,
inserted in a way that skip connections circumvent them. We investigate the
effect of skip connections and dropout. In addition, we propose a simple
problem-specific modification of the network objective function that restricts
the focus to the most important image regions and simplifies the optimization.
We train and validate our model using 882 CT scans and test on 1,000. Our
regularization techniques and objective improve the average Dice score by 7.1%,
yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
volumes and manual annotations.Comment: Accepted for MICCAI 201
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions
in computed tomography (CT) volumes. We build the model from two fully
convolutional networks, connected in tandem and trained together end-to-end. We
evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge,
attaining competitive liver and liver lesion detection and segmentation scores
across a wide range of metrics. Unlike other top performing methods, our model
output post-processing is trivial, we do not use data external to the
challenge, and we propose a simple single-stage model that is trained
end-to-end. However, our method nearly matches the top lesion segmentation
performance and achieves the second highest precision for lesion detection
while maintaining high recall.Comment: Late upload of conference version (ISBI
An automatic deep learning approach for coronary artery calcium segmentation
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and
cardiovascular events. In this work we present a system for the automatic
quantification of calcium score in ECG-triggered non-contrast enhanced cardiac
computed tomography (CT) images. The proposed system uses a supervised deep
learning algorithm, i.e. convolutional neural network (CNN) for the
segmentation and classification of candidate lesions as coronary or not,
previously extracted in the region of the heart using a cardiac atlas. We
trained our network with 45 CT volumes; 18 volumes were used to validate the
model and 56 to test it. Individual lesions were detected with a sensitivity of
91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%;
comparing calcium score obtained by the system and calcium score manually
evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A
high agreement (Cohen's k = 0.879) between manual and automatic risk prediction
was also observed. These results demonstrated that convolutional neural
networks can be effectively applied for the automatic segmentation and
classification of coronary calcifications
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