Song Y, Treanor D, Bulpitt A J, Wijayathunga N, Roberts N, Wilcox R, Magee D R
IEEE Trans Biomed Eng. 2014 Jan;61(1):96-108. doi: 10.1109/TBME.2013.2277777. Epub 2013 Aug 8.
Registration of histopathology images of consecutive tissue sections stained with different histochemical or immunohistochemical stains is an important step in a number of application areas, such as the investigation of the pathology of a disease, validation of MRI sequences against tissue images, multiscale physical modeling, etc. In each case, information from each stain needs to be spatially aligned and combined to ascertain physical or functional properties of the tissue. However, in addition to the gigabyte-size images and nonrigid distortions present in the tissue, a major challenge for registering differently stained histology image pairs is the dissimilar structural appearance due to different stains highlighting different substances in tissues. In this paper, we address this challenge by developing an unsupervised content classification method that generates multichannel probability images from a roughly aligned image pair. Each channel corresponds to one automatically identified content class. The probability images enhance the structural similarity between image pairs. By integrating the classification method into a multiresolution-block-matching-based nonrigid registration scheme (N. Roberts, D. Magee, Y. Song, K. Brabazon, M. Shires, D. Crellin, N. Orsi, P. Quirke, and D. Treanor, "Toward routine use of 3D histopathology as a research tool," Amer. J. Pathology, vol. 180, no. 5, 2012.), we improve the performance of registering multistained histology images. Evaluation was conducted on 77 histological image pairs taken from three liver specimens and one intervertebral disc specimen. In total, six types of histochemical stains were tested. We evaluated our method against the same registration method implemented without applying the classification algorithm (intensity-based registration) and the state-of-the-art mutual information based registration. Superior results are obtained with the proposed method.
对用不同组织化学或免疫组织化学染色剂染色的连续组织切片的组织病理学图像进行配准，在许多应用领域都是重要的一步，比如疾病病理学研究、针对组织图像验证MRI序列、多尺度物理建模等。在每种情况下，每种染色的信息都需要在空间上对齐并合并，以确定组织的物理或功能特性。然而，除了组织中存在的千兆字节大小的图像和非刚性畸变外，对不同染色的组织学图像对进行配准的一个主要挑战是，由于不同染色突出了组织中的不同物质，导致结构外观不同。在本文中，我们通过开发一种无监督内容分类方法来应对这一挑战，该方法从大致对齐的图像对生成多通道概率图像。每个通道对应一个自动识别的内容类别。概率图像增强了图像对之间的结构相似性。通过将分类方法集成到基于多分辨率块匹配的非刚性配准方案中（N. Roberts、D. Magee、Y. Song、K. Brabazon、M. Shires、D. Crellin、N. Orsi、P. Quirke和D. Treanor，“迈向将三维组织病理学作为研究工具的常规使用”，《美国病理学杂志》，第180卷，第5期，2012年），我们提高了对多染色组织学图像进行配准的性能。对取自三个肝脏标本和一个椎间盘标本的77对组织学图像进行了评估。总共测试了六种类型的组织化学染色剂。我们将我们的方法与未应用分类算法的相同配准方法（基于强度的配准）和基于互信息的最新配准方法进行了比较。所提出的方法获得了更好的结果。
