Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis

Abstract

Joint analysis of multiple protein expressions and tissue morphology patterns is important for disease diagnosis, treatment planning, and drug development, requiring cross-staining alignment of multiple immunohistochemical and histopathological slides. However, cross-staining alignment of enormous gigapixel whole slide images (WSIs) at single cell precision is difficult. Apart from gigantic data dimensions of WSIs, there are large variations on the cell appearance and tissue morphology across different staining together with morphological deformations caused by slide preparation. The goal of this study is to build an image registration framework for cross-staining alignment of gigapixel WSIs of histopathological and immunohistochemical microscopic slides and assess its clinical applicability. To the authors’ best knowledge, this is the first study to perform real time fully automatic cross staining alignment of WSIs with 40× and 20× objective magnification. The proposed WSI registration framework consists of a rapid global image registration module, a real time interactive field of view (FOV) localization model and a real time propagated multi-level image registration module. In this study, the proposed method is evaluated on two kinds of cancer datasets from two hospitals using different digital scanners, including a dual staining breast cancer data set with 43 hematoxylin and eosin (H&E) WSIs and 43 immunohistochemical (IHC) CK(AE1/AE3) WSIs, and a triple staining prostate cancer data set containing 30 H&E WSIs, 30 IHC CK18 WSIs, and 30 IHC HMCK WSIs. In evaluation, the registration performance is measured by not only registration accuracy but also computational time. The results show that the proposed method achieves high accuracy of 0.833 ± 0.0674 for the triple-staining prostate cancer data set and 0.931 ± 0.0455 for the dual-staining breast cancer data set, respectively, and takes only 4.34 s per WSI registration on average. In addition, for 30.23% data, the proposed method takes less than 1 s for WSI registration. In comparison with the benchmark methods, the proposed method demonstrates superior performance in registration accuracy and computational time, which has great potentials for assisting medical doctors to identify cancerous tissues and determine the cancer stage in clinical practice.

Assessing Local Descriptors for Feature-Based Registration of Whole-Slide Images

“…Over the years, various traditional methods have been developed in addition to SIFT, including SURF [7], ORB [8], FREAK [9], BRIEF [10] and BRISK [4]. In the field of digital pathology, SIFT remains the most commonly used local feature detector and descriptor [11]- [13]. However, in some cases, alternative methods such as SURF and ORB are employed alongside SIFT to ensure robustness in the event of failure [12].…”

“…This step can be further divided into: (i) matching, which establishes correspondences between the detected PoIs in the moving image and their counterparts in the fixed image, and (ii) robust estimation of the transformation between these sets of correspondences. RANSAC [18] is an extensively utilized robust algorithm in various disciplines, including digital pathology [13], [19], for identifying reliable correspondences among matched pairs.…”