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Weitz P, Valkonen M, Solorzano L, Carr C, Kartasalo K, Boissin C, Koivukoski S, Kuusela A, Rasic D, Feng Y, Pouplier SS, Sharma A, Eriksson KL, Robertson S, Marzahl C, Gatenbee CD, Anderson ARA, Wodzinski M, Jurgas A, Marini N, Atzori M, Müller H, Budelmann D, Weiss N, Heldmann S, Lotz J, Wolterink JM, De Santi B, Patil A, Sethi A, Kondo S, Kasai S, Hirasawa K, Farrokh M, Kumar N, Greiner R, Latonen L, Laenkholm A, Hartman J, Ruusuvuori P, Rantalainen M
Med Image Anal 97 (-) 103257 [2024-10-00; online 2024-07-01]
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.
PubMed 38981282
DOI 10.1016/j.media.2024.103257
Crossref 10.1016/j.media.2024.103257
pii: S1361-8415(24)00182-8