PDF(Pubmed)
Abstract：
This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place of a peer checking, when not possible or to give a rapid indication of infection to be referred for further treatment, thus saving time and resources. In contrast, manual contouring is a time-consuming process in which a professional would contour each patient one by one to be later checked by another professional. The proposed model uses approximately 49 k parameters while others average over 1,000 times more parameters. As our approach relies on a very compact model, shorter training times are observed, which make it possible to easily retrain the model using other data and potentially afford \"personalised medicine\" workflows. The model achieves similarity scores of Specificity (Sp) = 0.996 ± 0.001, Accuracy (Acc) = 0.994 ± 0.002 and Mean absolute error (MAE) = 0.0075 ± 0.0005.
摘要：
这项工作介绍了编码器-解码器卷积神经网络（ED-CNN）模型在自动分割COVID-19计算机断层扫描（CT）数据中的应用。通过这样做，我们正在产生一个替代当前文献的模型，这很容易跟踪和复制，使它更容易为现实世界的应用程序，因为很少的培训将需要使用它。我们简单的方法获得了与以前发表的研究相当的结果，使用更复杂的深度学习网络。我们展示了一种高质量的胸部CT扫描自动分割预测，可以正确描绘肺部感染区域。这种分割自动化可以用作加速轮廓过程的工具，要么检查手动轮廓，代替同行检查，当不可能或迅速给出感染指征时，将其转介进行进一步治疗，从而节省时间和资源。相比之下,手动轮廓绘制是一个耗时的过程，在这个过程中，专业人员会一个接一个地绘制每个患者的轮廓，然后由另一个专业人员进行检查。所提出的模型使用大约49k参数，而其他模型的平均参数超过1000倍。由于我们的方法依赖于一个非常紧凑的模型，观察到较短的训练时间，这使得使用其他数据轻松地重新训练模型成为可能，并可能提供“个性化医疗”工作流程。该模型获得特异性(Sp)=0.996±0.001、准确性(Acc)=0.994±0.002和平均绝对误差(MAE)=0.0075±0.0005的相似性得分。
