Semi-supervised learning has attracted great attention in the field of machine learning, especially for medical image segmentation tasks, since it alleviates the heavy burden of collecting abundant densely annotated data for training. However, most of existing methods underestimate the importance of challenging regions (e.g. small branches or blurred edges) during training. We believe that these unlabeled regions may contain more crucial information to minimize the uncertainty prediction for the model and should be emphasized in the training process. Therefore, in this paper, we propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Particularly, our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss by our designed cycled pseudo label scheme to encourage mutual consistency. Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively. Our MC-Net outperforms six recent semi-supervised methods for left atrium segmentation, and sets the new state-of-the-art performance on the LA database.

中文翻译：

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半监督左心房分割与相互一致性训练

半监督学习在机器学习领域尤其是医学图像分割任务中引起了极大的关注，因为它减轻了收集大量密集注释数据进行训练的沉重负担。但是，大多数现有方法低估了训练过程中具有挑战性的区域（例如小树枝或边缘模糊）的重要性。我们认为，这些未标记区域可能包含更多关键信息，以最大程度地减少模型的不确定性预测，因此应在训练过程中予以强调。因此，在本文中，我们提出了一种新颖的相互一致性网络（MC-Net），用于从3D MR图像进行半监督左心房分割。特别是，我们的MC-Net由一个编码器和两个略有不同的解码器组成，并且我们设计的循环伪标签方案将两个解码器的预测差异转换为无监督损失，以鼓励相互一致性。这种相互的一致性促使两个解码器具有一致且低熵的预测，并使模型能够逐渐捕获来自这些未标记挑战区域的广义特征。我们在公共左心房（LA）数据库上评估我们的MC-Net，它通过有效利用未标记的数据获得了令人印象深刻的性能提升。我们的MC-Net优于六种最近的半监督方法进行左心房分割，并在LA数据库上设置了最新的最新性能。这种相互的一致性促使两个解码器具有一致且低熵的预测，并使模型能够逐渐捕获来自这些未标记挑战区域的广义特征。我们在公共左心房（LA）数据库上评估我们的MC-Net，它通过有效利用未标记的数据获得了令人印象深刻的性能提升。我们的MC-Net优于六种最近的半监督方法进行左心房分割，并在LA数据库上设置了最新的最新性能。这种相互的一致性促使两个解码器具有一致且低熵的预测，并使模型能够逐渐捕获来自这些未标记挑战区域的广义特征。我们在公共左心房（LA）数据库上评估我们的MC-Net，它通过有效利用未标记的数据获得了令人印象深刻的性能提升。我们的MC-Net优于六种最近的半监督方法进行左心房分割，并在LA数据库上设置了最新的最新性能。