Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.

中文翻译：

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基于FCN的多图谱引导器官分割的标签校正。

最近，由于它们增强了针对不同对象之间变异性的鲁棒性，因此使用多个地图集对医学图像进行分割已引起了广泛的关注。这些基于图集的方法通常包括三个步骤：图集选择，图像配准以及最后的标签融合。图像配准是此过程的核心步骤之一，其准确性直接影响最终的标签性能。然而，由于受试者之间的解剖学差异，配准错误是不可避免的。本文的目的是开发一种基于深度学习的置信度估计方法，以减轻注册错误的潜在影响。我们首先提出一个带有残差连接的全卷积网络（FCN），以了解图像补丁对之间的关​​系（即 目标主体和地图集的补丁）和相关的标签置信补丁。使用获得的标签置信度补丁，我们可以识别变形的地图集标签中的潜在错误并进行纠正。然后，我们使用两种标签融合方法来融合校正后的地图集标签。提出的方法在海马分割的公开数据集上得到了验证。实验结果表明，我们提出的方法优于最新的分割方法。