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:10.48550/arXiv.2305.18033

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

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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 establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

Original language	English
Publisher	ArXiv.org
DOIs	https://doi.org/10.48550/arXiv.2305.18033
Publication status	Published - 29 May 2023

Keywords

eess.IV
cs.CV

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.48550/arXiv.2305.18033Licence: Other

PreprintFinal published version, 11.8 MBLicence: Other

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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue
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. & Marini, N. & 21 others, Atzori, M., Müller, H., Budelmann, D., Weiss, N., Heldmann, S., Lotz, J., Wolterink, J. M., De Santi, B., Patil, A., Sethi, A., Kondo, S., Kasai, S., Hirasawa, K., Farrokh, M., Kumar, N., Greiner, R., Latonen, L., Laenkholm, A. V., Hartman, J., Ruusuvuori, P. & Rantalainen, M., Oct 2024, In: Medical image analysis. 97, 103257.
Research output: Contribution to journal › Short survey › Academic › peer-review

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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, S. S., Sharma, A., Eriksson, K. L., Robertson, S., Marzahl, C., Gatenbee, C. D., Anderson, A. R. A., Wodzinski, M., Jurgas, A., ... Rantalainen, M. (2023). The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue. ArXiv.org. https://doi.org/10.48550/arXiv.2305.18033

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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 establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. ",

keywords = "eess.IV, cs.CV",

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 Santi, {Bruno De} 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 Anne-Vibeke Laenkholm and Johan Hartman and Pekka Ruusuvuori and Mattias Rantalainen",

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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 , Santi, BD, Patil, A, Sethi, A, Kondo, S, Kasai, S, Hirasawa, K, Farrokh, M, Kumar, N, Greiner, R, Latonen, L, Laenkholm, A-V, Hartman, J, Ruusuvuori, P & Rantalainen, M 2023 'The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue' ArXiv.org. https://doi.org/10.48550/arXiv.2305.18033

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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 - Santi, Bruno De

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 - 2023/5/29

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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 establish the current state-of-the-art in WSI registration 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 establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

KW - eess.IV

KW - cs.CV

U2 - 10.48550/arXiv.2305.18033

DO - 10.48550/arXiv.2305.18033

M3 - Preprint

BT - The ACROBAT 2022 Challenge

PB - ArXiv.org

ER -

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

Abstract

Keywords

UN SDGs

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Research output

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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