The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

•largest study to date on multi-stain histopathology image registration.•tissue originates from routine clinical workflows.•the contributions of eight teams are analysed and discussed in detail.•best-performing method approaches human annotator precision.•analysis of algorithms and covariates can gu...

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Bibliographic Details
Published in:Medical image analysis 2024-10, Vol.97, p.103257, Article 103257
Main Authors: Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R.A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, Rantalainen, Mattias
Format: Article
Language:English
Subjects:
Breast cancer
Computational pathology
Immunohistochemistry
Whole-slide-image registration
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Summary:•largest study to date on multi-stain histopathology image registration.•tissue originates from routine clinical workflows.•the contributions of eight teams are analysed and discussed in detail.•best-performing method approaches human annotator precision.•analysis of algorithms and covariates can guide future methods development. 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 provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.
ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2024.103257