The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

Philippe Weitz,Masi Valkonen,Leslie Solorzano,Circe Carr,Kimmo Kartasalo,Constance Boissin,Sonja Koivukoski,Aino Kuusela,Dusan Rasic,Yanbo Feng,Sandra Sinius Pouplier,Abhinav Sharma,Kajsa Ledesma Eriksson,Stephanie Robertson,Christian Marzahl,Chandler D. Gatenbee,Alexander R. A. Anderson,Marek Wodzinski,Artur Jurgas,Niccolò Marini,Manfredo Atzori,Henning Müller,Daniel Budelmann,Nick Weiss,Stefan Heldmann,Johannes Lotz,Jelmer M. Wolterink,Bruno De Santi,Abhijeet Patil,Amit Sethi,Satoshi Kondo,Satoshi Kasai,Kousuke Hirasawa,Mahtab Farrokh,Neeraj Kumar,Russell Greiner,Leena Latonen,Anne-Vibeke Laenkholm,Johan Hartman,Pekka Ruusuvuori,Mattias Rantalainen

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

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