Automatic Multi-Stain Registration of Whole Slide Images in Histopathology

• URL: http://arxiv.org/abs/2107.14292v1
• Date: Thu, 29 Jul 2021 19:38:30 GMT
• Title: Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
• Authors: Abubakr Shafique (1), Morteza Babaie (1 and 3), Mahjabin Sajadi (1), Adrian Batten (2), Soma Skdar (2), and H.R. Tizhoosh (1 and 3) ((1) Kimia Lab, University of Waterloo, Waterloo, ON, Canada., (2) Department of Pathology, Grand River Hospital, Kitchener, ON, Canada., and (3) Vector Institute, MaRS Centre, Toronto, Canada.)
• Abstract summary: automatic, and fast cross-staining alignment of enormous gigapixel Whole Slide Images (WSIs) at single-cell precision is challenging. In this paper, we propose a two-step automatic feature-based cross-staining alignment to assist localization of even tiny metastatic lymph node.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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