The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
This work establishes the state-of-the-art in WSI registration for breast cancer research and clinical applications, guiding method selection and development.
The ACROBAT challenge tackled the problem of aligning histopathological whole-slide-images (WSI) for breast cancer tissue, comparing eight registration algorithms on a dataset of 4,212 WSIs from 1,152 patients, finding that distinct methods can achieve high accuracy and identifying key covariates affecting performance.
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.