Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3850-3853. doi: 10.1109/EMBC46164.2021.9629532.
A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients.
提出了一种从 CT 图像中获得 COVID-19 相关病变体积累计估计的两步法。第一步包括应用 U-NET 卷积神经网络提供肺实质的分割。该架构使用可公开获取的用于基准胸部 CT 处理管道的患病肺部的胸腔量和胸腔积液分割（PleThora）数据集进行训练和验证。第二步包括使用基于概率主动轮廓（PACO）区域划定方法的自动算法获得体积病变估计。我们的管道成功地对 CT 图像中的 COVID-19 相关病变进行了分割，除了一些包括气道和血管在内的误标记区域。我们的工作流程应用于 50 名患者的图像中。
