Newson Kiri S, Benoit David M, Beavis Andrew W
Department of Physics and Mathematics, University of Hull, Hull, United Kingdom.
E. A. Milne Centre for Astrophysics, Department of Physics and Mathematics, University of Hull, Hull, United Kingdom.
PeerJ Comput Sci. 2024 Jul 23;10:e2178. doi: 10.7717/peerj-cs.2178. eCollection 2024.
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 = 0.996 ± 0.001, = 0.994 ± 0.002 and = 0.0075 ± 0.0005.
这项工作展示了编码器-解码器卷积神经网络（ED-CNN）模型在自动分割新冠病毒计算机断层扫描（CT）数据中的应用。通过这样做，我们正在生成一种与当前文献不同的模型，该模型易于理解和重现，由于使用时所需的训练很少，因此更便于实际应用。我们的简单方法取得了与先前发表的研究相当的结果，而先前的研究使用的是更复杂的深度学习网络。我们展示了对胸部CT扫描的高质量自动分割预测，该预测能够正确勾勒出肺部的感染区域。这种分割自动化可以用作一种工具来加速轮廓绘制过程，要么在不可能时替代同行检查来检查手动轮廓绘制，要么快速给出感染迹象以供进一步治疗参考，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，其中专业人员会逐个为每个患者绘制轮廓，之后由另一名专业人员进行检查。所提出的模型使用大约49k个参数，而其他模型平均使用的参数要多1000倍以上。由于我们的方法依赖于一个非常紧凑的模型，因此观察到训练时间更短，这使得使用其他数据轻松重新训练模型并潜在地实现“个性化医疗”工作流程成为可能。该模型的相似度得分分别为=0.996±0.001、=0.994±0.002和=0.0075±0.0005。
