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

doi:10.1016/j.media.2024.103257

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 MariniManfredo 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

Biolääketiede

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

2 Sitaatiot (Scopus)

9 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	103257
Julkaisu	Medical Image Analysis
Vuosikerta	97
DOI - pysyväislinkit	https://doi.org/10.1016/j.media.2024.103257
Tila	Julkaistu - lokak. 2024
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

Jufo-taso 3

!!ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

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10.1016/j.media.2024.103257Lisenssi: CC BY

1-s2.0-S1361841524001828-main-1Final published version, 9,5 MBLisenssi: CC BY

https://urn.fi/URN:NBN:fi:tuni-202408228243Lisenssi: CC BY

Siteeraa tätä

Weitz, P., Valkonen, M., Solorzano, L., Carr, C., Kartasalo, K., Boissin, C., Koivukoski, S., Kuusela, A., Rasic, D., Feng, Y., Pouplier, S. S., Sharma, A., Eriksson, K. L., Robertson, S., Marzahl, C., Gatenbee, C. D., Anderson, A. R. A., Wodzinski, M., Jurgas, A., ... Rantalainen, M. (2024). The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue. Medical Image Analysis, 97, Artikkeli 103257. https://doi.org/10.1016/j.media.2024.103257

@article{9d6682dad0ad4ebabce3434c540fc80b,

title = "The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue",

abstract = "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.",

keywords = "Breast cancer, Computational pathology, Immunohistochemistry, Whole-slide-image registration",

author = "Philippe Weitz and Masi Valkonen and Leslie Solorzano and Circe Carr and Kimmo Kartasalo and Constance Boissin and Sonja Koivukoski and Aino Kuusela and Dusan Rasic and Yanbo Feng and Pouplier, {Sandra Sinius} and Abhinav Sharma and Eriksson, {Kajsa Ledesma} and Stephanie Robertson and Christian Marzahl and Gatenbee, {Chandler D.} and Anderson, {Alexander R.A.} and Marek Wodzinski and Artur Jurgas and Niccol{\`o} Marini and Manfredo Atzori and Henning M{\"u}ller and Daniel Budelmann and Nick Weiss and Stefan Heldmann and Johannes Lotz and Wolterink, {Jelmer M.} and {De Santi}, Bruno and Abhijeet Patil and Amit Sethi and Satoshi Kondo and Satoshi Kasai and Kousuke Hirasawa and Mahtab Farrokh and Neeraj Kumar and Russell Greiner and Leena Latonen and Laenkholm, {Anne Vibeke} and Johan Hartman and Pekka Ruusuvuori and Mattias Rantalainen",

note = "Publisher Copyright: {\textcopyright} 2024",

year = "2024",

month = oct,

doi = "10.1016/j.media.2024.103257",

language = "English",

volume = "97",

journal = "Medical Image Analysis",

issn = "1361-8415",

publisher = "Elsevier B.V.",

}

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, AV, Hartman, J, Ruusuvuori, P & Rantalainen, M 2024, 'The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue', Medical Image Analysis, Vuosikerta 97, 103257. https://doi.org/10.1016/j.media.2024.103257

TY - JOUR

T1 - The ACROBAT 2022 challenge

T2 - Automatic registration of breast cancer tissue

AU - Weitz, Philippe

AU - Valkonen, Masi

AU - Solorzano, Leslie

AU - Carr, Circe

AU - Kartasalo, Kimmo

AU - Boissin, Constance

AU - Koivukoski, Sonja

AU - Kuusela, Aino

AU - Rasic, Dusan

AU - Feng, Yanbo

AU - Pouplier, Sandra Sinius

AU - Sharma, Abhinav

AU - Eriksson, Kajsa Ledesma

AU - Robertson, Stephanie

AU - Marzahl, Christian

AU - Gatenbee, Chandler D.

AU - Anderson, Alexander R.A.

AU - Wodzinski, Marek

AU - Jurgas, Artur

AU - Marini, Niccolò

AU - Atzori, Manfredo

AU - Müller, Henning

AU - Budelmann, Daniel

AU - Weiss, Nick

AU - Heldmann, Stefan

AU - Lotz, Johannes

AU - Wolterink, Jelmer M.

AU - De Santi, Bruno

AU - Patil, Abhijeet

AU - Sethi, Amit

AU - Kondo, Satoshi

AU - Kasai, Satoshi

AU - Hirasawa, Kousuke

AU - Farrokh, Mahtab

AU - Kumar, Neeraj

AU - Greiner, Russell

AU - Latonen, Leena

AU - Laenkholm, Anne Vibeke

AU - Hartman, Johan

AU - Ruusuvuori, Pekka

AU - Rantalainen, Mattias

PY - 2024/10

Y1 - 2024/10

N2 - 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.

AB - 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.

KW - Breast cancer

KW - Computational pathology

KW - Immunohistochemistry

KW - Whole-slide-image registration

U2 - 10.1016/j.media.2024.103257

DO - 10.1016/j.media.2024.103257

M3 - Article

AN - SCOPUS:85198009774

SN - 1361-8415

VL - 97

JO - Medical Image Analysis

JF - Medical Image Analysis

M1 - 103257

ER -

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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!!ASJC Scopus subject areas

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