Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immuno-histochemical and hematoxylin and eosin (H&E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.

中文翻译：

---

组织病理学中全玻片图像的自动多染色配准

多个生物标志物图像和组织形态的联合分析对于疾病诊断、治疗计划和药物开发非常重要。它需要在免疫组织化学和苏木精和伊红 (H&E) 显微玻片的全玻片图像 (WSI) 之间进行交叉染色比较。然而，以单细胞精度自动、快速地对巨大的千兆像素 WSI 进行交叉染色对齐具有挑战性。除了在载玻片制备过程中引入的形态变形外，不同染色的细胞外观和组织形态也存在很大差异。在本文中，我们提出了一种两步自动基于特征的交叉染色 WSI 对齐，以帮助在淋巴结评估中定位甚至微小的转移灶。图像对对齐，允许平移、旋转和缩放。通过首先使用尺度不变图像变换 (SIFT) 检测两个图像中的地标，然后使用快速样本一致性 (FSC) 协议来查找点对应关系并最终对齐图像，自动执行配准。使用 Jaccard 指数，使用视觉和定量标准评估配准结果。与手动注册相比，该系统产生的结果的平均 Jaccard 相似指数为 0.942。