The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks.

中文翻译：

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用于半监督 COVID-19 CT 分割的自集成协同训练框架


2019 年冠状病毒病 (COVID-19) 已成为严重的全球卫生紧急事件，并正在迅速传播。从计算机断层扫描 (CT) 扫描中分割新冠病灶对于监督疾病进展和进一步的临床治疗非常重要。由于标记 COVID-19 CT 扫描既费力又耗时，因此有必要开发一种基于有限标记数据的分割方法来执行此任务。在本文中，我们提出了一种自集成协同训练框架，通过有限的标记数据和大规模未标记数据进行训练，以自动从 CT 扫描中提取 COVID 病灶。具体来说，为了丰富无监督信息的多样性，我们构建了一个由两个协作模型组成的协同训练框架，其中两个模型在训练过程中使用各自预测的未标记数据的伪标签相互教学。此外，为了减轻噪声伪标签对每个模型的不利影响，我们提出了一种自集成策略，对未标记数据的最新预测进行一致性正则化，其中未标记数据的预测通过以下方式逐渐集成：每个训练时期结束时的移动平均值。我们在包含 103 个 CT 扫描的 COVID-19 数据集上评估我们的框架。实验结果表明，与最先进的半监督分割网络相比，我们提出的方法在仅 4 个标记 CT 扫描的情况下实现了更好的性能。