论文标题
基于3D U-NET
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net
论文作者
论文摘要
2019年冠状病毒病（COVID-19）影响了世界各地数十亿人的生命，并对公共医疗保健产生重大影响。由于对RT-PCR作为筛查方法的敏感性的怀疑不断增加，因此像计算机断层扫描一样的医学成像为替代方案提供了巨大的潜力。因此，高度期望自动图像分割作为定量评估和疾病监测的临床决策支持。但是，公开可用的COVID-19成像数据受到限制，从而导致传统方法过度拟合。为了解决此问题，我们为Covid-19受感染区域提出了创新的自动分割管道，该区域能够通过用作变体数据库来处理小型数据集。我们的方法着重于通过执行多种预处理方法并利用广泛的数据增强来实现训练的独特和随机图像贴片。为了进一步降低过度拟合的风险，我们实施了标准的3D U-NET体系结构，而不是新的或计算复杂的神经网络体系结构。通过对COVID-19患者的20张CT扫描进行5倍的交叉验证，我们能够为肺和COVID-19受感染区域开发高度准确且可靠的分割模型，而不会过度适用于有限的数据。我们的方法达到了肺的骰子相似性系数为0.956，感染的骰子相似性系数为0.761。我们证明了所提出的方法优于相关的方法，进步了COVID-19分段的最新方法，并使用有限的数据改善了医学图像分析。代码和模型可在以下链接下获得：https：//github.com/frankkramer-lab/covid19.miscnn
The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. Through a 5-fold cross-validation on 20 CT scans of COVID-19 patients, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on the limited data. Our method achieved Dice similarity coefficients of 0.956 for lungs and 0.761 for infection. We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves medical image analysis with limited data. The code and model are available under the following link: https://github.com/frankkramer-lab/covid19.MIScnn
