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

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.

CD3-CD8 immune score associated with a clinical score stratifies PDAC prognosis regardless of adjuvant or neoadjuvant chemotherapy

“… 25 Multiple single labeling on serial slides might be easier, but would still require systematic slide scanning followed by automated tissue registration. 26 Consequently, it is important to note that major improvements in wet and dry labs are needed to consider these kinds of approaches in routine practice.…”

Blueprints from plane to space: outlook of next‐generation three‐dimensional histopathology

“…To reconstruct 3D data, it is necessary to perform accurate slice-to-slice registration on the serialized sections, for which advanced algorithms have been developed (Figure 2B). 11,12 Nevertheless, this approach has begun to gather interest for its potential in 3D histopathology.…”

“…To reconstruct 3D data, it is necessary to perform accurate slice‐to‐slice registration on the serialized sections, for which advanced algorithms have been developed (Figure 2B ). 11 , 12 Nevertheless, this approach has begun to gather interest for its potential in 3D histopathology. In a representative study, researchers reconstructed continuous 3D H&E‐stained histological sections and, using machine learning segmentation algorithms, elucidated previously unobserved spatial relationships between neoplastic pancreatic cells and adjacent normal tissues (Figure 3A ).…”

Virtual alignment of pathology image series for multi-gigapixel whole slide images

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