Uncertainty-aware multi-view co-training for semi-supervise d me dical image segmentation and domain adaptation
Uncertainty-aware multi-view co-training for semi-supervise d me dical image segmentation and domain adaptation
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DOI：
10.1016/j.media.2020.101766
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发表时间：
2020-10-01
影响因子：
10.9
通讯作者：
Roth, Holger
中科院分区：
文献类型：
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作者：
Xia, Yingda;Yang, Dong;Roth, Holger
尽管在医学图像分割方面取得了巨大的成功，但基于深度学习的方法通常需要大量注释良好的数据，这在医学图像分析领域是非常昂贵的。另一方面，未标记的数据更容易获得。半监督学习和无监督领域自适应都利用了未标记数据，两者之间有着密切的联系。在本文中，我们提出了不确定性感知多视图协同训练（UMCT），这是一个统一的框架，解决了这两个任务的体积医学图像分割。我们的框架能够有效地利用未标记的数据以获得更好的性能。我们首先将3D体旋转并排列成多个视图，并在每个视图上训练3D深度网络。然后，我们通过在未标记的数据上强制多视图一致性来应用协同训练，其中利用每个视图的不确定性估计来实现准确的标记。在NIH胰腺分割数据集和多器官分割数据集上的实验表明，所提出的框架在半监督医学图像分割上具有最先进的性能。在无监督域自适应设置下，我们通过将我们的多器官分割模型应用于来自医学分割十项全能数据集的两个病理器官，验证了该工作的有效性。此外，我们表明我们的UMCT-DA模型甚至可以有效地处理标记源数据不可访问的具有挑战性的情况，展示了现实世界应用的强大潜力。(c) 2020 Elsevier B.V.版权所有
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the other hand, is much easier to acquire. Semi-supervised learning and unsupervised domain adaptation both take the advantage of unlabeled data, and they are closely related to each other. In this paper, we propose uncertainty-aware multi-view co-training (UMCT), a unified framework that addresses these two tasks for volumetric medical image segmentation. Our framework is capable of efficiently utilizing unlabeled data for better performance. We firstly rotate and permute the 3D volumes into multiple views and train a 3D deep network on each view. We then apply co-training by enforcing multi-view consistency on unlabeled data, where an uncertainty estimation of each view is utilized to achieve accurate labeling. Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation. Under unsupervised domain adaptation settings, we validate the effectiveness of this work by adapting our multi-organ segmentation model to two pathological organs from the Medical Segmentation Decathlon Datasets. Additionally, we show that our UMCT-DA model can even effectively handle the challenging situation where labeled source data is inaccessible, demonstrating strong potentials for real-world applications. (c) 2020 Elsevier B.V. All rights reserved.