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. 

We collected noncontrast thoracic CT images from our institution and manually segmented them. We then trained a U-Net (with the Pytorch framework) to perform semantic segmentation. The final state of the trained network is contained in this dataset.

This repository contains a .pth file which is the complete set of trained weights for the neural network. A repository of Python code contained at https://github.com/ucsd-fcrl/unet_deploy may be a useful starting point for using this U-Net.