Xiong Jing, Ren Jing, Luo Liqun, Horowitz Mark
Department of Electrical Engineering, Stanford University, Stanford, CA, United States.
Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA, United States.
Front Neuroinform. 2018 Dec 11;12:93. doi: 10.3389/fninf.2018.00093. eCollection 2018.
Histological brain slices are widely used in neuroscience to study the anatomical organization of neural circuits. Systematic and accurate comparisons of anatomical data from multiple brains, especially from different studies, can benefit tremendously from registering histological slices onto a common reference atlas. Most existing methods rely on an initial reconstruction of the volume before registering it to a reference atlas. Because these slices are prone to distortions during the sectioning process and often sectioned with non-standard angles, reconstruction is challenging and often inaccurate. Here we describe a framework that maps each slice to its corresponding plane in the Allen Mouse Brain Atlas (2015) to build a plane-wise mapping and then perform 2D nonrigid registration to build a pixel-wise mapping. We use the L2 norm of the histogram of oriented gradients difference of two patches as the similarity metric for both steps and a Markov random field formulation that incorporates tissue coherency to compute the nonrigid registration. To fix significantly distorted regions that are misshaped or much smaller than the control grids, we train a context-aggregation network to segment and warp them to their corresponding regions with thin plate spline. We have shown that our method generates results comparable to an expert neuroscientist and is significantly better than reconstruction-first approaches. Code and sample dataset are available at sites.google.com/view/brain-mapping.
组织学脑切片在神经科学中被广泛用于研究神经回路的解剖组织。对来自多个大脑（尤其是来自不同研究）的解剖数据进行系统且准确的比较，通过将组织学切片配准到一个通用参考图谱上可极大受益。大多数现有方法在将体积配准到参考图谱之前依赖于其初始重建。由于这些切片在切片过程中容易变形，且常常以非标准角度切片，重建具有挑战性且往往不准确。在此，我们描述了一个框架，该框架将每个切片映射到艾伦小鼠脑图谱（2015 年）中的对应平面以构建平面映射，然后执行二维非刚性配准以构建逐像素映射。我们在两个步骤中均使用两个小块的定向梯度差异直方图的 L2 范数作为相似性度量，并使用结合组织连贯性的马尔可夫随机场公式来计算非刚性配准。为了修复严重扭曲的区域（这些区域形状异常或比控制网格小得多），我们训练了一个上下文聚合网络，用薄板样条将它们分割并扭曲到其对应区域。我们已经表明，我们的方法生成的结果与专业神经科学家的结果相当，并且明显优于先重建的方法。代码和示例数据集可在 sites.google.com/view/brain-mapping 上获取。
