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.心脏计算机断层扫描（Cardiac computed tomography）在冠状动脉疾病评估与冠状动脉钙化（coronary artery calcium, CAC）的临床评价中具有明确的临床应用价值，但电离辐射的使用限制了其临床推广。束形“蝶形”过滤器（Beam-shaping “bow-tie” filters）通过向等中心（isocenter）区域递送更高剂量的X射线注量，决定了辐射剂量与有效扫描视野直径（SFOV）。将心脏定位在等中心附近的方法，可实现缩小扫描视野的成像，并降低心脏扫描的辐射剂量。我们开发了一种可实现心脏居中并缩小扫描视野的预测方法。在该项研究过程中，我们采用U-Net对非增强胸部CT扫描图像进行分割，以估算相关的辐射剂量降低效果。本文公开了该U-Net网络。具体而言，本数据集包含一个经训练的U-Net（卷积神经网络，convolutional neural network），其训练目标为分割非增强胸部计算机断层扫描图像。