This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT image-based diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patch-based segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%.

翻译：本文建议对肺部感染区采用一种与COVID-19病人的CT量相隔的分解方法。COVID-19在全世界传播,造成许多感染病人和死亡。对COVID-19的CT图像诊断可以提供快速和准确的诊断结果。肺部感染区的自动分解方法提供了诊断的定量标准。以前的方法使用整个 2D 图像或 3D 量程序。感染区在规模上有很大差异。这种过程很容易忽略小感染区。基于补丁程序对于分割小目标是有效的。然而,选择适当的补丁大小在感染区分解方面是困难的。我们利用不同可接受的截断区大小的图像诊断结果来获取感染区。我们利用可接受的FCN的图像分析的大小的大小的不确定性。 接受的字段规模可以界定为补丁大小和数量,从补丁缩缩缩缩缩缩缩的缩略图。