Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences(CAS), Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Med Image Anal. 2019 Jan;51:157-168. doi: 10.1016/j.media.2018.10.012. Epub 2018 Nov 1.
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.
多图谱基方法常用于磁共振脑图像标记，减轻了神经影像学分析研究中手动标记的负担和耗时任务。传统上，多图谱基方法首先将多个图谱注册到目标图像，然后将标签从标记的图谱传播到未标记的目标图像。然而，配准步骤涉及非刚性对齐，这通常很耗时，并且可能缺乏高精度。或者，基于补丁的方法在放宽对准确配准的需求方面显示出了前景，但它们通常需要使用手工制作的特征。最近，深度学习技术通过从训练图像中自动学习全面的外观特征，在图像标记方面展示了其有效性。在本文中，我们提出了一种用于自动图像标记的多图谱引导全卷积网络（MA-FCN），旨在借助训练图谱的先验知识进一步提高标记性能。具体来说，我们以基于补丁的方式训练我们的 MA-FCN 模型，其中输入数据不仅包括训练图像补丁，还包括其一组相邻（即最相似）仿射对齐的图谱补丁。来自相邻图谱补丁的指导信息可以帮助提高学习的 FCN 的判别能力。在不同数据集上的实验结果表明，我们提出的方法是有效的，明显优于传统的 FCN 和几种最先进的磁共振脑标记方法。
