Fang Longwei, Zhang Lichi, Nie Dong, Cao Xiaohuan, Bahrami Khosro, He Huiguang, Shen Dinggang
Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Patch Based Tech Med Imaging (2017). 2017 Sep;10530:12-19. doi: 10.1007/978-3-319-67434-6_2. Epub 2017 Aug 31.
Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting direct assistance from the atlases. To address these limitations, in this paper, we propose a multi-atlas guided 3D fully convolutional networks (FCN) for brain image labeling. Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. Experiments show that the use of multi-atlas guidance improves the brain labeling performance.
脑图像中解剖结构的自动标注在神经影像分析中起着重要作用。在所有方法中，基于多图谱的分割方法因其在传播先验标注信息方面的稳健性而被广泛使用。然而，总是需要进行非线性配准，这很耗时。另外，基于补丁的方法已被提出以放宽图像配准的要求，但标注通常由目标图像信息独立确定，而没有从图谱中获得直接帮助。为了解决这些局限性，在本文中，我们提出了一种用于脑图像标注的多图谱引导的3D全卷积网络（FCN）。具体而言，在网络学习过程中纳入了基于多图谱的引导。基于此，FCN的判别能力得到增强，最终有助于准确预测。实验表明，使用多图谱引导可提高脑标注性能。
