Abstract

In 2020 the outbreak of Covid-19 influenced lives of billions of people all around the globe and motivated governments of different countries to revisit the current situation with regards to public healthcare systems and to methods used in modern medicine. As the workload on radiologists and physicians increased, so did the demand on systems that automatically analyse medical images and detect pathologies. Many current computer vision papers assume that the solution would be integrated into a healthcare system. However improvement according to “classic” metrics like mAP or IoU does not necessarily mean improvement from the radiologist’s point of view. In this paper we suggest that while calculating metrics, averaging should be performed not by all studies, but by different groups of studies, in order to be close to human perception of a quality of a segmentation. And that we should count the number of false positive components, found outside lungs, because the presence of such components is negatively perceived by radiologists. Also we propose a method that improves the segmentation of lung pathologies and pleural effusion according to the points given above.

中文翻译：

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Covid-19 患者 CT 扫描肺病理和胸腔积液分割的改进

摘要

2020 年，Covid-19 的爆发影响了全球数十亿人的生活，并促使不同国家的政府重新审视公共医疗保健系统和现代医学方法的现状。随着放射科医生和医生工作量的增加，对自动分析医学图像和检测病理的系统的需求也在增加。许多当前的计算机视觉论文都假设该解决方案将集成到医疗保健系统中。然而，从放射科医生的角度来看，根据“经典”指标（如 mAP 或 IoU）的改进并不一定意味着改进。在本文中，我们建议在计算指标时，不应由所有研究进行平均，而应由不同的研究组进行平均，为了接近人类对分割质量的感知。并且我们应该计算在肺外发现的假阳性成分的数量，因为放射科医生对这些成分的存在有负面看法。我们还提出了一种根据上述要点改进肺部病理和胸腔积液分割的方法。