Twofold binary image consensus for medical imaging meta-analysis
- Author
- Carlos Lopez Molina, Javier Sanchez Ruiz de Gordoa, Victoria Zelaya-Huerta and Bernard De Baets (UGent)
- Organization
- Abstract
- In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. This procedure raises questions on how to compare the results obtained by automatic algorithms to multiple ground truth items. Secondarily, it raises questions on the meaning of the divergences between experts. In this work, we focus on the case of immunohistochemistry image segmentation and analysis. We propose measures to quantify the divergence in groups of ground truth images, and we observe their behaviour. These measures are based upon fusion techniques for binary images, which is a common example of non-monotone data fusion process. Our measures can be used not only in this specific field of medical imagery, but also in any task related to meta-quality evaluation for image processing, e.g. ground truth validation or expert rating.
- Keywords
- Data fusion, Twofold Consensus Ground Truth, Medical imagery, Immunohistochemistry (IHC), Meta-analysis, TRUTH
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8628846
- MLA
- Lopez Molina, Carlos, et al. “Twofold Binary Image Consensus for Medical Imaging Meta-Analysis.” Information Processing and Management of Uncertainty in Knowledge-Based Systems : Theory and Foundations, Pt II, edited by Jesús Medina et al., vol. 854, Springer, 2018, pp. 389–400, doi:10.1007/978-3-319-91476-3_33.
- APA
- Lopez Molina, C., Ruiz de Gordoa, J. S., Zelaya-Huerta, V., & De Baets, B. (2018). Twofold binary image consensus for medical imaging meta-analysis. In J. Medina, M. Ojeda-Aciego, J. L. Verdegay, D. A. Pelta, I. P. Cabrera, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information processing and management of uncertainty in knowledge-based systems : theory and foundations, pt II (Vol. 854, pp. 389–400). https://doi.org/10.1007/978-3-319-91476-3_33
- Chicago author-date
- Lopez Molina, Carlos, Javier Sanchez Ruiz de Gordoa, Victoria Zelaya-Huerta, and Bernard De Baets. 2018. “Twofold Binary Image Consensus for Medical Imaging Meta-Analysis.” In Information Processing and Management of Uncertainty in Knowledge-Based Systems : Theory and Foundations, Pt II, edited by Jesús Medina, Manuel Ojeda-Aciego, José Luis Verdegay, David A Pelta, Inma P Cabrera, Bernadette Bouchon-Meunier, and Ronald R Yager, 854:389–400. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-319-91476-3_33.
- Chicago author-date (all authors)
- Lopez Molina, Carlos, Javier Sanchez Ruiz de Gordoa, Victoria Zelaya-Huerta, and Bernard De Baets. 2018. “Twofold Binary Image Consensus for Medical Imaging Meta-Analysis.” In Information Processing and Management of Uncertainty in Knowledge-Based Systems : Theory and Foundations, Pt II, ed by. Jesús Medina, Manuel Ojeda-Aciego, José Luis Verdegay, David A Pelta, Inma P Cabrera, Bernadette Bouchon-Meunier, and Ronald R Yager, 854:389–400. Berlin, Germany: Springer. doi:10.1007/978-3-319-91476-3_33.
- Vancouver
- 1.Lopez Molina C, Ruiz de Gordoa JS, Zelaya-Huerta V, De Baets B. Twofold binary image consensus for medical imaging meta-analysis. In: Medina J, Ojeda-Aciego M, Verdegay JL, Pelta DA, Cabrera IP, Bouchon-Meunier B, et al., editors. Information processing and management of uncertainty in knowledge-based systems : theory and foundations, pt II. Berlin, Germany: Springer; 2018. p. 389–400.
- IEEE
- [1]C. Lopez Molina, J. S. Ruiz de Gordoa, V. Zelaya-Huerta, and B. De Baets, “Twofold binary image consensus for medical imaging meta-analysis,” in Information processing and management of uncertainty in knowledge-based systems : theory and foundations, pt II, Cadiz, Spain, 2018, vol. 854, pp. 389–400.
@inproceedings{8628846,
abstract = {{In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. This procedure raises questions on how to compare the results obtained by automatic algorithms to multiple ground truth items. Secondarily, it raises questions on the meaning of the divergences between experts. In this work, we focus on the case of immunohistochemistry image segmentation and analysis. We propose measures to quantify the divergence in groups of ground truth images, and we observe their behaviour. These measures are based upon fusion techniques for binary images, which is a common example of non-monotone data fusion process. Our measures can be used not only in this specific field of medical imagery, but also in any task related to meta-quality evaluation for image processing, e.g. ground truth validation or expert rating.}},
author = {{Lopez Molina, Carlos and Ruiz de Gordoa, Javier Sanchez and Zelaya-Huerta, Victoria and De Baets, Bernard}},
booktitle = {{Information processing and management of uncertainty in knowledge-based systems : theory and foundations, pt II}},
editor = {{Medina, Jesús and Ojeda-Aciego, Manuel and Verdegay, José Luis and Pelta, David A and Cabrera, Inma P and Bouchon-Meunier, Bernadette and Yager, Ronald R}},
isbn = {{9783319914756}},
issn = {{1865-0929}},
keywords = {{Data fusion,Twofold Consensus Ground Truth,Medical imagery,Immunohistochemistry (IHC),Meta-analysis,TRUTH}},
language = {{eng}},
location = {{Cadiz, Spain}},
pages = {{389--400}},
publisher = {{Springer}},
title = {{Twofold binary image consensus for medical imaging meta-analysis}},
url = {{http://doi.org/10.1007/978-3-319-91476-3_33}},
volume = {{854}},
year = {{2018}},
}
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