Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and charateristics of the tumor microenvironment can be observed and quantified.
翻译:组织样本中含血层的细小部分使得有可能通过在每一部分中为一个新的蛋白质进行污点来研究蛋白表达和肿瘤异质性等地方差异。为了比较不同蛋白的图象并与之相关,必须非常精确地记录图象。我们想要解决的问题是整个幻灯片图象的登记。这提出了三个挑战:(一) 图像非常大;(二) 薄片导致工艺品,使得全球的松叶丝登记容易发生非常大的地方错误;(三) 需要当地方蜡登记,以保存正确的组织形态(地方大小、形状和质)。在我们的处理方法中,我们比较以全图象或自然次区域(而不是平方形图)自动和手工特征选择为基础的世界方块登记。与自然次区域合作,通过互动工具可以排除含有与科学无关信息的区域。我们还提出了一种新的方法,通过注册信任图(RCM)将地方登记质量直观化为当地登记质量。通过这种方法,可以观测到肿瘤的内质和量化。