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.