Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease. In this paper we adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of slices of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage. Our modelswere trained on data from different medical centers. We compared predictions of our models with those of six experiencedradiologists and our segmentation model outperformed most of them. On the task of classification of disease severity, ourmodel outperformed all the radiologists.
翻译:对胸部CT扫描的分析可用于检测受COVID-19等传染病影响的肺部部分。确定受损伤的肺体积对于按病情的严重程度制定治疗建议和确定病人的优先次序至关重要。在这份文件中,我们采用了一种基于使用深层革命性网络组合来截断肺部CT扫描片段的方法。利用我们的模型,我们能够分解损伤、评估病人动态、估计受损伤影响的肺体相对体积并评估肺损伤阶段。我们对不同医疗中心的数据进行了模型培训。我们比较了我们模型的预测与6个有经验的放射学家的预测,我们的分解模型在疾病严重性分类方面优于所有放射学家。