U-net for automated thoracic CT semantic segmentation

Cardiac computed tomography has a clear clinical role in the
evaluation of coronary artery disease and assessment of coronary
artery calcium (CAC) but the use of ionizing radiation limits the
clinical use.
Beam-shaping “bow-tie” filters determine the
radiation dose and the effective scan field-of-view diameter
(SFOV) by delivering higher X-ray fluence to a region
centered at the isocenter. A method for positioning the heart
near the isocenter could enable reduced SFOV imaging and reduce
dose in cardiac scans. We developed a predictive approach to center the
heart and reduce the SFOV. As part of this effort, we used a UNet to
segment noncontrast thoracic CT scans to estimate the associated dose
reductions. Here we publish the UNet network. Specifically, this dataset
contains a trained U-net (convolutional neural network) which was trained
for the purpose of segmenting noncontrast thoracic computed tomography
images.