Khan Azrin, Garner Rachael, Rocca Marianna La, Salehi Sana, Duncan Dominique
Laboratory of Neuro Imaging, Keck School of Medicine of USC, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA USA.
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA USA.
Signal Image Video Process. 2023;17(4):907-914. doi: 10.1007/s11760-022-02183-6. Epub 2022 Mar 28.
Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision ( ) and specificity ( ) scores. Furthermore, the proposed method generated PLAs with a difference of from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.
自2019年12月以来，新型冠状病毒肺炎（COVID-19）已导致全球超过375万人死亡。因此，准确的COVID-19诊断和分类方法对于促进患者的快速治疗和终止病毒传播至关重要。肺部感染分割有助于识别可能支持快速诊断、严重程度评估和患者预后预测的独特感染模式，但手动分割耗时且依赖于放射学专业知识。人们已经探索了基于深度学习的方法来减轻分割负担；然而，由于缺乏用于建立基本事实的大型公开注释数据集，它们的准确性受到限制。出于这些原因，我们提出了一种基于阈值的半自动分割方法，以生成在肺部计算机断层扫描（CT）图像上可见的感染区域的感兴趣区域（ROI）分割。然后使用感染掩码来计算肺部异常百分比（PLA），以确定COVID-19的严重程度，并分析后续CT中的疾病进展。与其他COVID-19 ROI分割方法相比，所提出的方法平均实现了更高的精度（ ）和特异性（ ）分数。此外，所提出的方法生成的PLA与真实PLA的差异为 。改进的ROI分割结果表明，所提出的方法有潜力协助放射科医生评估感染严重程度并分析后续CT中的疾病进展。
