Schwier Michael, Böhler Tobias, Hahn Horst Karl, Dahmen Uta, Dirsch Olaf
Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany.
J Pathol Inform. 2013 Mar 30;4(Suppl):S10. doi: 10.4103/2153-3539.109868. Print 2013.
The registration of histological whole slide images is an important prerequisite for modern histological image analysis. A partial reconstruction of the original volume allows e.g. colocalization analysis of tissue parameters or high-detail reconstructions of anatomical structures in 3D.
In this paper, we present an automatic staining-invariant registration method, and as part of that, introduce a novel vessel-based rigid registration algorithm using a custom similarity measure. The method is based on an iterative best-fit matching of prominent vessel structures.
We evaluated our method on a sophisticated synthetic dataset as well as on real histological whole slide images. Based on labeled vessel structures we compared the relative differences for corresponding structures. The average positional error was close to 0, the median for the size change factor was 1, and the median overlap was 0.77.
The results show that our approach is very robust and creates high quality reconstructions. The key element for the resulting quality is our novel rigid registration algorithm.
组织学全切片图像的配准是现代组织学图像分析的重要前提。对原始体积进行部分重建可实现例如组织参数的共定位分析或三维解剖结构的高细节重建。
在本文中,我们提出了一种自动的染色不变配准方法,并在此过程中引入了一种使用自定义相似性度量的基于血管的新型刚性配准算法。该方法基于突出血管结构的迭代最佳拟合匹配。
我们在一个复杂的合成数据集以及真实的组织学全切片图像上评估了我们的方法。基于标记的血管结构,我们比较了相应结构的相对差异。平均位置误差接近0,大小变化因子的中位数为1,中位数重叠率为0.77。
结果表明我们的方法非常稳健,并能创建高质量的重建。产生这种质量的关键因素是我们的新型刚性配准算法。