In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network ( CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.

中文翻译：

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半监督医学图像分割的不一致性感知不确定性估计

在半监督医学图像分割中，大多数先前的工作都借鉴了一个普遍的假设，即更高的熵意味着更高的不确定性。在本文中，我们研究了一种估计不确定性的新方法。我们观察到，当在一定程度上分配不同的误分类代价时，如果一个像素的分割结果变得不一致，这个像素在其分割中表现出相对的不确定性。因此，我们提出了一种新的半监督分割模型，即保守激进网络（ CoraNet 简而言之）基于我们的不确定性估计和单独的自我训练策略。特别是，我们的CoraNet 模型由三个主要部分组成：保守激进模块 (CRM)、特定区域分割网络 (C-SN) 和不确定区域分割网络 (UC-SN)结束方式。我们使用公开可用的基准数据集广泛评估了我们在各种分割任务上的方法，包括 ACDC 数据集上的 CT 胰腺、MR 心内膜和 MR 多结构分割。与目前的技术水平相比，我们的CoraNet 展示了卓越的性能。此外，我们还分析了它与半监督医学图像分割中传统的不确定性估计方法的联系和区别。