A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
The ACROBAT 2022 Challenge : Automatic Registration Of Breast Cancer Tissue
Tekijät: 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
Kustantaja: Elsevier
Julkaisuvuosi: 2024
Journal: Medical Image Analysis
Tietokannassa oleva lehden nimi: Medical Image Analysis
Artikkelin numero: 103257
Vuosikerta: 97
ISSN: 1361-8415
eISSN: 1361-8431
DOI: https://doi.org/10.1016/j.media.2024.103257
Verkko-osoite: https://doi.org/10.1016/j.media.2024.103257
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/457116338
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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We acknowledge support from Stratipath and Karolinska Institutet sponsoring the ACROBAT challenge prizes; MICCAI society for hosting the ACROBAT challenge, and Nguyen Thuy Duong Tran for support with digitizing histopathology slides. We acknowledge funding from: Vetenskapsrådet (Swedish Research Council) Cancerfonden (Swedish Cancer Society) ERA PerMed (ERAPERMED2019-224-ABCAP) MedTechLabs Swedish e-science Research Centre (SeRC) VINNOVA SweLife Academy of Finland (#341967, #334782, #335976, #334774) Cancer Foundation Finland University of Turku Graduate School Turku University Foundation Oskar Huttunen Foundation David and Astrid Hägelén Foundation Orion Research Foundation KI Research Foundation This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 945358. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA (www.imi.europe.eu).
