2 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