Background and objective

The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.

Methods

In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision.

Results

Lungs’ segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes.

Conclusions

The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation.

中文翻译：

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CoLe-CNN+：上下文学习 - 用于 COVID-19-Ground-Glass-Opacities 检测和分割的卷积神经网络

 背景和目标

人群范围内的 COVID-19 识别最常用的工具是逆转录聚合酶链反应测试，该测试可检测拭子样本中喉咙（或痰）中是否存在病毒。该测试的灵敏度在 59 %到 71 %之间。然而，该测试并不能提供有关肺部感染范围的准确信息。此外，事实证明，通过读取计算机断层扫描 (CT) 扫描，临床医生可以更全面地了解疾病的严重程度。因此，我们提出了一个全面的系统，用于根据CT扫描进行全自动 COVID-19 检测和病变分割，该系统由深度学习策略提供支持，以支持 COVID-19 诊断的决策过程。

 方法

在提出的工作流程中，输入的 CT 图像首先经过肺部勾画，然后进行 COVID-19 检测，最后进行病灶分割。所选的神经网络具有 U 形架构，使用新引入的多重卷积层结构，在新颖的管道中生成肺部分割掩模，用于直接 COVID-19 检测和分割。此外，我们提出了一个定制的损失函数，保证灵敏度和精度之间的平均最佳平衡。

 结果

肺的分割结果显示灵敏度接近 99 % ，Dice 得分为 97 % 。经过10次不同的运行后，检测网络没有观察到误报，平均准确率为97.1 % 。病灶分割的平均准确度约为99 % 。使用 UNet 作为基准，我们将我们的结果与文献中提出的几种其他技术进行比较，获得了相对 UNet 结果的最大改进。

 结论

本文提出的方法优于从 CT 图像中进行 COVID-19 病灶分割的最先进方法，并将 UNet 的 F1 分数结果提高了 38.2 % 。这项工作中观察到的高精度为我们的算法在与医学图像分割相关的其他领域中提供了广泛的可能应用。