Publications and Citations

If you use any material from the CHAOS challenge, we kindly ask to give appropriate credit to the following works:

[1] The challenge article.
[2] The Dataset.
[3] Performance comparison between automatic and interactive methods on limited data (Our previous challenge).
{[4] is not ready yet.}

[1] A.E. Kavur, N.S. Gezer, M. Barış, S. Aslan, P.-H. Conze, et al. "CHAOS Challenge - combined (CT-MR) Healthy Abdominal Organ Segmentation",  Medical Image Analysis, Volume 69, 2021.


title = {{CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation}},
journal = {Medical Image Analysis},
publisher= {Elsevier BV},
volume = {69},
pages = {101950},
year = {2021},
month = Apr,
issn = {1361-8415},
doi = {},
url = {},
author = {A. Emre Kavur and N. Sinem Gezer and Mustafa Barış and Sinem Aslan and Pierre-Henri Conze and Vladimir Groza and Duc Duy Pham and Soumick Chatterjee and Philipp Ernst and Savaş Özkan and Bora Baydar and Dmitry Lachinov and Shuo Han and Josef Pauli and Fabian Isensee and Matthias Perkonigg and Rachana Sathish and Ronnie Rajan and Debdoot Sheet and Gurbandurdy Dovletov and Oliver Speck and Andreas Nürnberger and Klaus H. Maier-Hein and Gözde {Bozdağı Akar} and Gözde Ünal and Oğuz Dicle and M. Alper Selver},
keywords = {Segmentation, Challenge, Abdomen, Cross-modality},

[2] A.E. Kavur, M. A. Selver, O. Dicle, M. Barış,  N.S. Gezer. CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Apr.  2019. Zenodo.

  author       = {Ali Emre Kavur and M. Alper Selver and Oğuz Dicle and Mustafa Barış and  N. Sinem Gezer},
  title        = {{CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data}},
  month        = Apr,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v1.03},
  doi          = {10.5281/zenodo.3362844},
  url          = {}

[3] A.E. Kavur, N.S. Gezer, M. Barış, Y.Şahin, S. Özkan, B. Baydar, et al.  "Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors", Diagnostic and  Interventional  Radiology,  vol. 26, pp. 11–21, Jan. 2020.

  title = {Comparison of semi-automatic and deep learning based automatic methods for liver segmentation in living liver transplant donors},
  author = {Kavur, A. Emre and Gezer, Naciye Sinem  and Barış, Mustafa and Şahin, Yusuf and Özkan, Savaş and Baydar,Bora and Yüksel, Ulaş and Kılıkçıer, Çağlar and Olut, Şahin and Bozdağı Akar, Gözde and Ünal, Gözde and Dicle, Oğuz and Selver, M. Alper},
  journal = {Diagnostic and Interventional Radiology},
  volume = {26},
  pages = {11-21},
  year = {2020},
  month = Jan,
  doi = {10.5152/dir.2019.19},
  url = {}

[4] Future publication:

Besides providing a comparative study of a range of algorithms, the competition will also be used to gain insight about complementarity and diversity of different methods. Recent developments show that classifier ensembles can provide higher performance than its components if certain conditions are met. Accordingly, the results of participating methods will not only be evaluated according to their segmentation performance, but also be analyzed with quantitative diversity measures. The organizers believe that this information will spark many ideas to improve existing ensemble strategies and a second journal paper will be prepared for this purpose.