School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
Comput Biol Med. 2022 May;144:105340. doi: 10.1016/j.compbiomed.2022.105340. Epub 2022 Mar 11.
The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.
COVID-19 的爆发导致医疗资源严重短缺。自 2020 年以来，胸部 CT 扫描的磨玻璃样混浊（GGO）和实变一直是影像学诊断的重要基础。COVID-19 和其他肺炎之间的影像学特征相似，使得难以区分它们，并影响放射科医生的诊断。最近，COVID-19 的深度学习主要分为疾病分类和病变分割，但很少有工作关注这两个任务之间的特征相关性。为了解决这些问题，在本研究中，我们提出了 MultiR-Net，这是一种用于 COVID-19 分类和病变分割的 3D 深度学习模型，以实现实时和可解释的 COVID-19 胸部 CT 诊断。具体来说，所提出的网络由两个子网组成：用于病变分割的多尺度特征融合 UNet 样子网和用于疾病诊断的分类子网。两个子网之间的特征通过反向注意力机制和可迭代训练策略进行融合。同时，我们提出了一种损失函数来增强两个子网之间的交互。单个指标不能完全反映网络的有效性。因此，我们使用各种评估指标（如平均表面距离、体积 Dice 等）对数据集进行分割结果的量化。我们使用包含 275 个 3D CT 扫描的数据集来对 COVID-19、社区获得性肺炎（CAP）和健康人群进行分类，并对肺炎患者的病变进行分割。我们将数据集分为 70%和 30%用于训练和测试。大量实验表明，我们的多任务模型框架在分类测试集上的平均召回率为 93.323%，平均精度为 94.005%，在我们数据集的分割测试集上的体积 Dice 得分为 69.95%。
