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.
翻译:在半监督医疗图象分割中,大多数先前的作品都基于以下共同假设,即高摄氏度意味着更高的不确定性。在本文中,我们调查了一种估算不确定性的新方法。我们观察到,如果像素的分解结果不一致,这种像素显示其分解的相对不确定性。因此,我们根据我们的不确定性估计和单独的自我培训战略,提出了一个新的半监督分解模型,即保守-激进网络(简称CoraNet),特别是我们的CoraNet模型由三个主要组成部分组成:保守-激进模块(CRM)、某种区域分解网络(C-SN)和可以以端到端方式培训的不确定区域分解网络(UC-SN)。我们广泛评价了我们利用公开的基准数据集,包括CT 丙烯、MRM 内心和 AS-MRM(MRM) 多结构分解方法。我们C DCDC数据集的模型由三大部分组成:保守-激进模块(CRMM)、某种区域分解网络(CRM)、某种区域分解网络(C-SNation)和我们常规分解法的分解方法(我们与常规分解的分解方法的分解),也显示了我们医学分解方法。