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Muscle histology image analysis for sarcopenia: Registration of successive sections with distinct atpase activity

机译:肌肉减少症的肌肉组织学图像分析:具有不同atpase活性的连续切片的配准

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One way of evaluating muscle quality is to determine its fiber type composition in histological sections. A complete muscle fiber type characterization system requires combining information from successive muscle histology images with different ATPase stain. Due to the local and global deformations introduced in slide preparation process, a precise non-rigid registration is essential to construct the spatial correspondences between these successive images. This study proposes an approach for automated non-rigid registration of successive muscle histological sections. We propose a feature-based registration that uses a two stage approach: a rigid initialization followed by a non-rigid refinement. The rigid initialization step globally aligns successive tissue slides by finding correspondences between individually segmented muscle fibers using Fourier shape descriptors and computing the global rigid transformation using a voting scheme tolerant to mismatches. In the nonrigid stage we establish precise point correspondences using the normalized cross correlation metric and compute the non-rigid distortion using a polynomial transformation that minimizes the mean square distance between these control points.
机译:评估肌肉质量的一种方法是在组织学切片中确定其纤维类型组成。完整的肌肉纤维类型表征系统需要将来自连续肌肉组织学图像的信息与不同的ATPase染色相结合。由于在幻灯片准备过程中引入了局部和全局变形,因此精确的非刚性配准对于构建这些连续图像之间的空间对应关系至关重要。这项研究提出了一种自动的非刚性连续肌肉组织切片的配准方法。我们提出了一种基于特征的注册,该注册使用了两个阶段的方法:刚性初始化,然后进行非刚性优化。刚性初始化步骤通过使用傅立叶形状描述符找到各个分段的肌肉纤维之间的对应关系,并使用容错的投票方案来计算全局刚性变换,从而全局对齐连续的组织玻片。在非刚性阶段,我们使用归一化互相关度量建立精确的点对应关系,并使用多项式变换来计算非刚性失真,该变换将这些控制点之间的均方距离最小化。

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