Improving Algorithm for the Alignment of Consecutive, Whole-Slide, Immunohistochemical Section Images

Cher-Wei Liang; Ruey-Feng Chang; Pei-Wei Fang; Chiao-Min Chen

doi:10.4103/jpi.jpi_106_20

Improving Algorithm for the Alignment of Consecutive, Whole-Slide, Immunohistochemical Section Images

J Pathol Inform. 2021 Aug 3:12:29. doi: 10.4103/jpi.jpi_106_20. eCollection 2021.

Authors

Cher-Wei Liang^{1

2

3}, Ruey-Feng Chang^{4

5

6}, Pei-Wei Fang¹, Chiao-Min Chen⁴

Affiliations

¹ Department of Pathology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan.
² School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.
³ Department and Graduate Institute of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁴ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
⁵ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
⁶ MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan.

Abstract

Background: Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images.

Materials and methods: An automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices.

Results: Sets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 μm with a standard deviation of 14.94 μm, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 μm with 50.85 μm standard deviation).

Conclusions: Our method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expected.

Keywords: Cell-level resolution; Image registration; Immunohistochemistry; Multidimensional graph-based registration; Proteome topology; Three-dimensional tissue reconstruction; Whole-slide images.