Papers

The first article resulting from the Surgical Data Science workshop 2016 is published in Nature Biomedical Engineering: https://rdcu.be/K0ng

It provides a consensus definition for Surgical Data Science, identifies associated challenges and opportunities and provides a roadmap for advancing the field.

The article can be cited with the DOI 10.1038/s41551-017-0132-7 as follows:

Lena Maier-Hein, Swaroop S. Vedula, Stefanie Speidel, Nassir Navab, Ron Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes Kenngott, Michael Kranzfelder, Anand Malpani, Keno März, Thomas Neumuth, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, Gregory D. Hager and Pierre Jannin. Surgical data science for next-generation interventions. Nature Biomedical Engineering 1(9), 691 (2017). DOI 10.1038/s41551-017-0132-7

BIBTEX

@article{maier-hein_sds_2017,
    title = {Surgical data science for next-generation interventions},
    volume = {1},
    issn = {2157-846X},
    url = {https://doi.org/10.1038/s41551-017-0132-7},
    doi = {10.1038/s41551-017-0132-7},
    number = {9},
    journal = {Nature Biomedical Engineering},
    author = {Maier-Hein, Lena and Vedula, Swaroop S. and Speidel, Stefanie and Navab, Nassir and Kikinis, Ron and Park, Adrian and Eisenmann, Matthias and Feussner, Hubertus and Forestier, Germain and Giannarou, Stamatia and Hashizume, Makoto and Katic, Darko and Kenngott, Hannes and Kranzfelder, Michael and Malpani, Anand and März, Keno and Neumuth, Thomas and Padoy, Nicolas and Pugh, Carla and Schoch, Nicolai and Stoyanov, Danail and Taylor, Russell and Wagner, Martin and Hager, Gregory D. and Jannin, Pierre},
    month = sep,
    year = {2017},
    pages = {691–696}
}

 

An extended long version of the paper is also available: https://arxiv.org/abs/1806.03184

BIBTEX

@article{maier-hein_sds_2018,
    title = {Surgical {Data} {Science}: {A} {Consensus} {Perspective}},
    journal = {arXiv e-prints},
    author = {Maier-Hein, Lena and Eisenmann, Matthias and Feldmann, Carolin and Feussner, Hubertus and Forestier, Germain and Giannarou, Stamatia and Gibaud, Bernard and Hager, Gregory D. and Hashizume, Makoto and Katic, Darko and Kenngott, Hannes and Kikinis, Ron and Kranzfelder, Michael and Malpani, Anand and März, Keno and Müller-Stich, Beat and Navab, Nassir and Neumuth, Thomas and Padoy, Nicolas and Park, Adrian and Pugh, Carla and Schoch, Nicolai and Stoyanov, Danail and Taylor, Russell and Wagner, Martin and Swaroop Vedula, S. and Jannin, Pierre and Speidel, Stefanie},
    month = jun,
    year = {2018},
    keywords = {Computer Science – Computers and Society},
    pages = {arXiv:1806.03184}
}

 

The article resulting from the second edition of the workshop on Surgical Data Science is published on arXiv: https://arxiv.org/abs/2011.02284

BIBTEX

@article{maier-hein_sds_2020,
    title = {Surgical {Data} {Science} — from {Concepts} to {Clinical} {Translation}},
    url = {http://arxiv.org/abs/2011.02284},
    urldate = {2020-11-05},
    journal = {arXiv:2011.02284 [cs, eess]},
    author = {Maier-Hein, Lena and Eisenmann, Matthias and Sarikaya, Duygu and März, Keno and Collins, Toby and Malpani, Anand and Fallert, Johannes and Feussner, Hubertus and Giannarou, Stamatia and Mascagni, Pietro and Nakawala, Hirenkumar and Park, Adrian and Pugh, Carla and Stoyanov, Danail and Vedula, Swaroop S. and Müller, Beat Peter and Cleary, Kevin and Fichtinger, Gabor and Forestier, Germain and Gibaud, Bernard and Grantcharov, Teodor and Hashizume, Makoto and Kenngott, Hannes and Kikinis, Ron and Mündermann, Lars and Navab, Nassir and Onogur, Sinan and Sznitman, Raphael and Taylor, Russell and Tizabi, Minu Dietlinde and Wagner, Martin and Hager, Gregory D. and Neumuth, Thomas and Padoy, Nicolas and Jannin, Pierre and Speidel, Stefanie},
    month = oct,
    year = {2020},
    note = {arXiv: 2011.02284},
    keywords = {Computer Science – Computers and Society, Computer Science – Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science – Image and Video Processing, Computer Science – Machine Learning}
}