论文标题
肺CT中covid-19病变的无标签分割
Label-Free Segmentation of COVID-19 Lesions in Lung CT
论文作者
论文摘要
带注释的图像的稀缺性阻碍了自动化解决方案的建立，以可靠的COVID-19诊断和评估CT。为了减轻数据注释的负担，我们在本文中提出了一种无标签的方法，可通过像素级异常建模在CT中分割COVID-19病变，从而从正常的CT肺扫描中挖掘出相关的知识。我们的建模灵感来自于观察到的气管和血管的部分，这些部分位于病变所属的高强度范围内，表现出强大的模式。为了促进在像素级别学习此类模式，我们使用一组令人惊讶的简单操作合成“病变”，并将合成的“病变”插入正常的CT肺扫描中以形成训练对，从中，我们从中学习了一个正常的转换网络（NORMNET），从而变成了“ Abrandormal”图像的正常形象。我们在三个不同数据集上的实验验证了Normnet的有效性，这显着优于各种无监督的异常检测方法（UAD）方法。
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a pixel level, we synthesize `lesions' using a set of surprisingly simple operations and insert the synthesized `lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-converting network (NormNet) that turns an 'abnormal' image back to normal. Our experiments on three different datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods.
