An early effective screening and grading of COVID-19 has become imperative towards optimizing the limited available resources of the medical facilities. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Composite Deep network with Feature Weighting (CDNetFW), is proposed for efficient delineation of infected regions from lung CT images. Initially a coarser-segmentation is performed directly at shallower levels, thereby facilitating discovery of robust and discriminatory characteristics in the hidden layers. The novel feature weighting module helps prioritise relevant feature maps to be probed, along with those regions containing crucial information within these maps. This is followed by estimating the severity of the disease.The deep network CDNetFW has been shown to outperform several state-of-the-art architectures in the COVID-19 lesion segmentation task, as measured by experimental results on CT slices from publicly available datasets, especially when it comes to defining structures involving complex geometries.

翻译：早期对COVID-19进行有效筛查和分级对于优化医疗设施有限资源分配至关重要。对肺部CT中感染体积的自动分割有望显著辅助患者的诊断与护理。然而，由于病灶在肺内具有不规则结构和位置，精确界定病变区域仍存在问题。本文提出一种新型深度学习架构——结合特征加权的复合深度网络（CDNetFW），用于从肺部CT图像中高效分割感染区域。该方法首先在浅层直接执行粗分割，从而促进隐藏层中稳健且具判别性特征的发现。新颖的特征加权模块有助于优先探查相关特征图以及图中包含关键信息的区域，随后进行疾病严重程度评估。实验结果表明，在公开数据集CT切片上，深度网络CDNetFW在COVID-19病灶分割任务中优于多种先进架构，尤其在界定具有复杂几何形状的结构时表现更佳。