影像学在2019年冠状病毒病(COVID-19)的临床管理中起着关键作用,因为影像学检查结果反映了肺部的病理过程。胸部高分辨率计算机断层扫描的视觉分析可以区分COVID-19的实质异常,这对于检测和量化以获得准确的疾病分层和预后至关重要。然而,视觉评估和量化对放射科医师来说是一项耗时的任务。在这方面,半自动分割工具,例如基于卷积神经网络的算法,可以通过描绘其轮廓来促进病理病变的检测。在这项工作中,我们比较了4种基于编码器-解码器模式的最先进的卷积神经网络,用于COVID-19感染的二进制分割,并在从比萨大学医院数据库收集的90例诊断为COVID-19的患者的HRCT体积扫描中进行了测试.更确切地说,我们从一个基本模型开始,著名的UNet,然后我们增加了一个注意机制来获得一个注意-UNet,最后,我们采用了递归范式来创建一个递归残差UNet(R2-UNet)。在后一种情况下,我们还在R2-UNet的解码路径中添加了关注门,因此设计了一个R2-AttentionUNet,以使特征表示和积累更有效。我们将它们进行了比较,以了解可以使神经模型在此任务中获得最佳性能的认知机制以及数据量之间的良好折衷,时间,和所需的计算资源。我们建立了五倍交叉验证,并通过评估Dice得分方面的性能来评估这些模型的优势和局限性,Precision,和回忆定义在2D图像和整个3D体积。从分析结果来看,可以得出结论,Attention-UNet优于其他模型,达到81.93%的最佳性能,在2D骰子得分方面,在测试装置上。此外,我们进行了统计分析以评估模型之间的性能差异.我们的发现表明,在UNet架构中整合复发机制会导致模型对我们特定应用的有效性下降。
Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known
UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual
UNet (R2-
UNet). In the latter case, we also added attention gates to the decoding path of an R2-
UNet, thus designing an R2-Attention
UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the
UNet architecture leads to a decline in the model\'s effectiveness for our particular application.