National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
Cytometry A. 2021 Jun;99(6):575-585. doi: 10.1002/cyto.a.24332. Epub 2021 Mar 18.
The alignment of a 2D microscopic image stack to create a 3D image volume is an indispensable aspect of serial section electron microscopy (EM) technology, which could restore the original 3D integrity of biological tissues destroyed by chemical fixation and physical dissection. However, due to the similar texture intrasection and complex variations intersections of neural images, previous registration methods usually failed to yield reliable correspondences. And this also led to misalignment and impeded restoring the z-axis anatomical continuity of the neuron volume. In this article, inspired by human behaviors in finding correspondences, which use the topological relationship of image contents, we developed a spatial attention-based registration method for serial EM images to improve registration accuracy. Our approach combined the U-Net framework with spatial transformer networks (STN) to regress corresponding transformation maps in an unsupervised training fashion. The spatial attention (SA) module was incorporated into the U-Net architecture to increase the distinctiveness of image features by modeling its topological relationship. Experiments are conducted on both simulated and real data sets (MAS and RegCremi). Quantitative and qualitative comparisons demonstrate that our approach results in state of art accuracy (using the evaluation index of NCC, SSIM, Dice, Landmark error) and providing smooth and reliable transformation with less texture blur and unclear boundary than existing techniques. Our method is able to restore image stacks for visualization and quantitative analysis of EM image sequences.
二维显微镜图像堆栈的配准对于创建三维图像体至关重要，它可以恢复因化学固定和物理切割而破坏的生物组织的原始 3D 完整性。然而，由于神经图像的切片内纹理相似和复杂的变化交点，先前的配准方法通常无法产生可靠的对应关系。这也导致了配准错误，并阻碍了神经元体积的 z 轴解剖连续性的恢复。在本文中，受人类在寻找对应关系时利用图像内容拓扑关系的启发，我们开发了一种基于空间注意力的串行 EM 图像配准方法，以提高配准精度。我们的方法结合了 U-Net 框架和空间变换网络（STN），以无监督的方式回归相应的变换图。空间注意力（SA）模块被合并到 U-Net 架构中，通过对其拓扑关系进行建模来增加图像特征的可区分性。我们在模拟和真实数据集（MAS 和 RegCremi）上进行了实验。定量和定性比较表明，我们的方法在使用 NCC、SSIM、Dice、Landmark 误差等评估指标的情况下达到了最先进的准确性，并且与现有技术相比，提供了更平滑、更可靠的变换，纹理模糊和边界不清晰的情况更少。我们的方法能够恢复图像堆栈，用于可视化和定量分析 EM 图像序列。
