Automatic Registration of Breast Cancer Tissue: Structured description of the challenge design
Creators: name: Weitz, Philippe
name: Masi Valkonen
name: Hartman, Johan
name: Ruusuvuori, Pekka
name: Rantalainen, Mattias
Publisher: name: Zenodo
Year: 2022
Subjects: registration
WSI
histology
breast cancer
whole-slide-image
multi-stain
immunohistochemistry
IHC
MICCAI
challenge

description: The ACROBAT challenge aims to advance the development of whole-slide-image (WSI) registration algorithms that can align WSIs of tissue sections that were stained with immunohistochemistry (IHC) to corresponding tissue regions that were stained with haematoxylin and eosin (H&amp;E). In order to evaluate usefulness of developed methods on real-world data, all slides in this challenge originate from routine diagnostic workflows. <br> Registration across multiple stains can be an enabling technology both for research and diagnostics. There are e.g. several studies [1]–[4] that use IHC as a local label to train subsequent models to predict from H&amp;E WSIs. In diagnostic settings, it may be interesting to overlay WSIs of differently stained tissue to investigate patterns of correspondence and divergence between stains and to evaluate e.g. intra-tumor heterogeneity. Unfortunately, WSI registration is particularly challenging due to the gigapixel scale of these images. Furthermore, a few micrometers thin tissue sections easily deform during sample preparation and it is possible that parts of the tissue in WSIs of the same sample are not contained in other WSIs of that same sample, particularly if sections are not<br> consecutive. The appearance of tissue can also differ considerably depending on the stain. Multi-stain WSI registration is therefore an active field of research and has previously been addressed in the ANHIR [5] challenge, which has led to the publication of algorithms and code for several registration methods. However, it is unclear how the competing and other current WSI registration methods would perform on a realworld data set from routine clinical workflows, where artifacts such as glass cracks, pen marks or glue spills are common. Furthermore, the broad application of registration algorithms requires solutions that do not rely on a large number of manually generated paired landmarks for algorithm training but that can be optimized only with matched images. We therefore aim to release a data set for the ACROBAT challenge that originates from routine diagnostics to assess real-world applicability. The training data set will contain WSIs from 750 cases, with one H&amp;E WSI per case and on average 3 IHC WSIs out of four routine diagnostic stains (H&amp;E, ER, PR, HER2, KI67), resulting in 2250 image pairs between H&amp;E and immunohistochemistry (IHC) WSIs or ca. 5200 combinations between all stains. This increase of training data by more than one order of magnitude compared to previous WSI registration data sets may enable novel registration algorithms that can be optimized without the supervision of manually generated landmarks. Algorithm performance can be estimated with a validation data set with 100 cases and evaluated with a test set with 300 cases, consisting of one H&amp;E and IHC WSI each, for which paired landmarks will be generated.<br> We therefore hope that the ACROBAT challenge can contribute to the development and publication of robust registration tools that can be an enabling technology for future research and diagnostics. References [1] W. Bulten et al., “Epithelium segmentation using deep learning in H&amp;E-stained prostate specimens with immunohistochemistry as reference standard,” Sci. Rep., vol. 9, no. 1, p. 864, Jan. 2019.<br> [2] D. Tellez et al., “Whole-Slide Mitosis Detection in H&amp;E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks,” IEEE Trans. Med. Imaging, Mar. 2018, doi:10.1109/TMI.2018.2820199.<br> [3] P. Gamble et al., “Determining breast cancer biomarker status and associated morphological features using deep learning,” Communications Medicine, vol. 1, no. 1, pp. 1–12, Jul. 2021.<br> [4] M. Valkonen et al., “Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67,” IEEE Trans. Med. Imaging, vol. 39, no. 2, pp. 534–542, Feb. 2020.<br> [5] J. Borovec et al., “ANHIR: Automatic Non-Rigid Histological Image Registration Challenge,” IEEE Trans. Med. Imaging, vol. 39, no. 10, pp. 3042–3052, Oct. 2020.