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ImageCLEFmed Tuberculosis



Welcome to the 5th edition of the Tuberculosis Task!

Tuberculosis (TB) is a bacterial infection caused by a germ called Mycobacterium tuberculosis. About 130 years after its discovery, the disease remains a persistent threat and a leading cause of death worldwide according to WHO. This bacteria usually attacks the lungs, but it can also damage other parts of the body. Generally, TB can be cured with antibiotics. However, the different types of TB require different treatments, and therefore the detection of the TB type and characteristics are important real-world tasks.

Task history and lessons learned:

  • Tuberculosis task exists in ImageCLEF since 2017 and it was modified from year to year.
  • In the first and second editions of this task, held at ImageCLEF 2017 and ImageCLEF 2018, participants had to detect Multi-drug resistant patients (MDR subtask) and to classify the TB type (TBT subtask) both based only on the CT image. After 2 editions we concluded that the MDR subtask was not possible to solve based only on the image. In the TBT subtask, there was a slight improvement in 2018 with respect to 2017 on the classification results, however, not enough considering the amount of extra data provided in the 2018 edition, both in terms of new images and meta-data.
  • In the 3d edition Tuberculosis task was restructured to allow usage of uniform dataset, and included two subtasks - continued Severity Score (SVR) prediction subtask and a new subtask based on providing an automatic report (CT Report) on the TB case. In the 4th edition, the SVR subtask was dropped and automated CT report generation task was modified to be lung-based rather than CT-based.

Because of fairly high results achieved by the participants in the CTR task last year, the task organizers have decided to discontinue the CTR task at the moment and switch to the task which was not yet solved with high accuracy. It was decided to bring back to life the Tuberculosis Type classification task from the 1st and 2nd ImageCLEFmed Tuberculosis editions. The task dataset is updated in this year's edition. The dataset was extended in size, and some additional information is available for part of the CT scans.

We hope that utilizing the recent Machine Learning and Deep Learning methods will allow the participants to achieve much better results for the TB Type classification compared to the early editions of the task: 2017 and 2018. Here we encourage the participants to use any kind of methods and additional data which can be useful for the automatic classification of the TB Type. The participants are free to use general-purpose pre-trained models (e.g. using ImageNet), or to pre-train the models with the use of a 3rd-party dataset of CT-images, or to use a 3rd-party solution to pre-process the CT-scans of the current subtask.
Here are some resources that may be worth your attention:
* Catalog of trained models by NVIDIA:
* Segmentation of anatomical structures in different modalities including CT:
* Pulmonary Fibrosis Progression Challenge on Kaggle:
* Segmentation of COVID-19 lesions on Grand-Challenge:
* Other Kaggle CT-related datasets and competitions:
Please pay attention to the distribution license for each resource if you use it.

The task is available on the AIcrowd.

Preliminary schedule

  • 13 January 2021: Task web-page goes live
  • 26 February 2021: Release of the development data
  • 26 April 2021: Release of the test data
  • 30 April 2021: Registration closes
  • 7 May 2021: Run submission deadline
  • 17 May 2021: Release of the processed results by the task organizers
  • 21 May 2021: Submission of participant papers [CEUR-WS]
  • 21 May – 11 June 2021: Review process of participant papers
  • 11 June 2021: Notification of acceptance
  • 2 July 2021: Camera ready copy of participant papers and extended lab overviews [CEUR-WS]
  • 21-24 September 2021: The CLEF Conference, Bucharest, Romania.

Task description

The goal of this subtask is to automatically categorize each TB case into one of the following five types: (1) Infiltrative, (2) Focal, (3) Tuberculoma, (4) Miliary, (5) Fibro-cavernous.


In this edition, a dataset containing chest CT scans of 1338 TB patients is used: 917 images for the Training (development) data set and 421 for the Test set. Some of the scans are accompanied by additional meta-information, which may vary depending on data available for different cases. Each CT-image can correspond to only one TB type at a time. In this edition, there is each CT-scan corresponds to one patient.

For all patients we provide a single 3D CT image with an image size per slice of 512×512 pixels and number of slices being around 100. All the CT images are stored in NIFTI file format with .nii.gz file extension (g-zipped .nii files). This file format stores raw voxel intensities in Hounsfield units (HU) as well the corresponding image metadata such as image dimensions, voxel size in physical units, slice thickness, etc. A freely-available tool called "VV" can be used for viewing the image files. Currently, there are various tools available for reading and writing NIFTI files. Among them there are load_nii and save_nii functions for Matlab and Niftilib library for C, Java, Matlab and Python, NiBabel package for Python.

Moreover, for all the CT images we provide two versions of automatically extracted masks of the lungs. These data can be downloaded together with the patients CT images. The description of the first version of segmentation can be found here. The description of the second version of segmentation can be found here. The first version of segmentation provides more accurate masks, but it tends to miss large abnormal regions of lungs in the most severe TB cases. The second segmentation on the contrary provides more rough bounds, but behaves more stable in terms of including lesion areas. In case the participants use the provided masks in their experiments, please refer to the section "Citations" at the end of this page to find the appropriate citation for the corresponding lung segmentation technique.

Evaluation methodology

The results will be evaluated using unweighted Cohen’s Kappa (sample Matlab code).


DISCLAIMER : The results presented below have not yet been analyzed in-depth and are shown "as is". The results are sorted by descending kappa score.
Submission ID Group name Kappa Accuracy Rank
135715 SenticLab.UAIC 0.221 0.466 1
135539 SenticLab.UAIC 0.205 0.449 2
135768 SenticLab.UAIC 0.203 0.458 3
135720 hasibzunair 0.200 0.423 4
134526 hasibzunair 0.200 0.423 4
133556 hasibzunair 0.198 0.404 5
135766 SenticLab.UAIC 0.194 0.444 6
135552 SenticLab.UAIC 0.192 0.444 7
135721 SDVA-UCSD 0.190 0.371 8
134984 SenticLab.UAIC 0.187 0.404 9
134983 SenticLab.UAIC 0.183 0.401 10
135689 Emad_Aghajanzadeh 0.181 0.404 11
135792 Emad_Aghajanzadeh 0.181 0.404 11
135725 SDVA-UCSD 0.179 0.371 12
135395 SenticLab.UAIC 0.177 0.378 13
135722 SDVA-UCSD 0.177 0.363 14
134990 SenticLab.UAIC 0.174 0.397 15
135779 Emad_Aghajanzadeh 0.173 0.409 16
135762 SDVA-UCSD 0.171 0.359 17
135393 SenticLab.UAIC 0.169 0.366 18
135717 hasibzunair 0.164 0.390 19
135724 SDVA-UCSD 0.163 0.354 20
135728 SDVA-UCSD 0.155 0.361 21
134347 Emad_Aghajanzadeh 0.141 0.401 22
134939 MIDL-NCAI-CUI 0.140 0.333 23
134343 MIDL-NCAI-CUI 0.140 0.333 23
132806 hasibzunair 0.137 0.423 24
135793 Emad_Aghajanzadeh 0.136 0.371 25
135788 Emad_Aghajanzadeh 0.130 0.359 26
135729 SDVA-UCSD 0.130 0.352 27
135708 uaic2021 0.129 0.333 28
135783 Emad_Aghajanzadeh 0.122 0.340 29
134688 IALab_PUC 0.120 0.401 30
133407 KDE-lab 0.117 0.382 31
134345 MIDL-NCAI-CUI 0.098 0.354 32
134535 KDE-lab 0.086 0.373 33
133421 KDE-lab 0.085 0.375 34
133459 KDE-lab 0.081 0.373 35
135425 Emad_Aghajanzadeh 0.074 0.371 36
133456 KDE-lab 0.069 0.371 37
134551 hasibzunair 0.069 0.361 38
134161 hasibzunair 0.068 0.397 39
135785 Emad_Aghajanzadeh 0.056 0.342 40
134094 KDE-lab 0.038 0.344 41
134791 JBTTM 0.038 0.221 42
135061 Emad_Aghajanzadeh 0.036 0.373 43
133807 KDE-lab 0.017 0.378 44
133964 KDE-lab 0.017 0.378 44
133609 KDE-lab 0.016 0.382 45
135798 uaic2021 0.016 0.330 46
133103 Zhao_Shi_ 0.015 0.380 47
133102 Zhao_Shi_ 0.008 0.375 48
133802 KDE-lab 0.006 0.366 49
132984 Zhao_Shi_ 0.004 0.387 50
134415 hasibzunair 0.004 0.375 51
134344 MIDL-NCAI-CUI 0.004 0.252 52
135756 uaic2021 -0.003 0.366 53
133099 Zhao_Shi_ -0.006 0.382 54
132983 Zhao_Shi_ -0.006 0.382 54
133288 YNUZHOU -0.008 0.385 55
135750 uaic2021 -0.019 0.359 56
134300 MIDL-NCAI-CUI -0.022 0.197 57
135081 IALab_PUC -0.040 0.337 58
135787 IALab_PUC -0.048 0.245 59

CEUR Woring Notes

  • All participating teams with at least one graded submission, regardless of the score, should submit a CEUR working notes paper.
  • Official detailed instructions for the CLEF 2021 working notes can be found here:


    • When referring to the ImageCLEFtuberculosis 2021 task general goals, general results, etc. please cite the following publication (also referred to as ImageCLEF tuberculosis task overview):
      • Serge Kozlovski, Vitali Liauchuk, Yashin Dicente Cid, Vassili Kovalev, Henning Müller, Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification, CLEF working notes, CEUR, 2021.
      • BibTex:
          author = {Kozlovski, Serge and Liauchuk, Vitali and Dicente Cid, Yashin and Kovalev, Vassili and M\"uller, Henning},
          title = {Overview of {ImageCLEFtuberculosis} 2021 - {CT}-based Tuberculosis Type Classification},
          booktitle = {CLEF2021 Working Notes},
          series = {{CEUR} Workshop Proceedings},
          year = {2021},
          volume = {},
          publisher = { $$},
          pages = {},
          month = {September 21-24},
          address = {Bucharest, Romania}
      • When referring to the ImageCLEF 2021 lab general goals, general results, etc. please cite the following publication which will be published by September 2021 (also referred to as ImageCLEF general overview):
        • Bogdan Ionescu, Henning Müller, Renaud Péteri, Asma Ben Abacha, Mourad Sarrouti, Dina Demner-Fushman, Sadid A. Hasan, Serge Kozlovski,
          Vitali Liauchuk, Yashin Dicente, Vassili Kovalev, Obioma Pelka, Alba García Seco de Herrera, Janadhip Jacutprakart, Christoph M. Friedrich,
          Raul Berari, Andrei Tauteanu, Dimitri Fichou, Paul Brie, Mihai Dogariu, Liviu Daniel Ştefan, Mihai Gabriel Constantin, Jon Chamberlain, Antonio Campello, Adrian Clark, Thomas A. Oliver, Hassan Moustahfid, Adrian Popescu, Jérôme Deshayes-Chossart, Overview of the ImageCLEF 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications, in Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the 12th International Conference of the CLEF Association (CLEF 2021), Bucharest, Romania, Springer Lecture Notes in Computer Science LNCS, September 21-24, 2021
        • BibTex:
          author = {Bogdan Ionescu and Henning M\"uller and Renaud P\’{e}teri
          and Asma {Ben Abacha} and Mourad Sarrouti and Dina Demner-Fushman and
          Sadid A. Hasan and Serge Kozlovski and Vitali Liauchuk and Yashin
          Dicente and Vassili Kovalev and Obioma Pelka and Alba Garc\’{\i}a Seco
          de Herrera and Janadhip Jacutprakart and Christoph M. Friedrich and
          Raul Berari and Andrei Tauteanu and Dimitri Fichou and Paul Brie and
          Mihai Dogariu and Liviu Daniel \c{S}tefan and Mihai Gabriel Constantin
          and Jon Chamberlain and Antonio Campello and Adrian Clark and Thomas
          A. Oliver and Hassan Moustahfid and Adrian Popescu and J\’{e}r\^{o}me
          title = {{Overview of the ImageCLEF 2021}: Multimedia Retrieval in
          Medical, Nature, Internet and Social Media Applications},
          booktitle = {Experimental IR Meets Multilinguality, Multimodality, and
          series = {Proceedings of the 12th International Conference of the CLEF
          Association (CLEF 2021)},
          year = {2021},
          volume = {},
          publisher = {{LNCS} Lecture Notes in Computer Science, Springer},
          pages = {},
          month = {September 21-24},
          address = {Bucharest, Romania}
  • When using any of the provided mask of the lungs, please cite the corresponding publication:
      for segmentation #1 (fully automatic multistage):
      • Yashin Dicente Cid, Oscar A. Jiménez-del-Toro, Adrien Depeursinge, and Henning Müller. Efficient and fully automatic segmentation of the lungs in CT volumes. In: Goksel, O., et al. (eds.) Proceedings of the VISCERAL Challenge at ISBI. No. 1390 in CEUR Workshop Proceedings (Apr 2015)
      • BibTex:


          Title = {Efficient and fully automatic segmentation of the lungs in CT volumes},
          Booktitle = {Proceedings of the {VISCERAL} Anatomy Grand Challenge at the 2015 {IEEE ISBI}},
          Author = {Dicente Cid, Yashin and Jim{\'{e}}nez del Toro, Oscar Alfonso and Depeursinge, Adrien and M{\"{u}}ller, Henning},
          Editor = {Goksel, Orcun and Jim{\'{e}}nez del Toro, Oscar Alfonso and Foncubierta-Rodr{\'{\i}}guez, Antonio and M{\"{u}}ller, Henning},
          Keywords = {CAD, lung segmentation, visceral-project},
          Month = may,
          Series = {CEUR Workshop Proceedings},
          Year = {2015},
          Pages = {31-35},
          Publisher = { $<$$>$},
          Location = {New York, USA}
    • for segmentation #2 (via non-rigid image registration):
      • Liauchuk, V., Kovalev, V. ImageCLEF 2017: Supervoxels and co-occurrence for tuberculosis CT image classification. In: CLEF2017 Working Notes. CEUR Workshop Proceedings, Dublin, Ireland, (September 11-14 2017) (URL:
      • BibTex:


          Title = {ImageCLEF 2017: Supervoxels and co-occurrence for tuberculosis CT image classification},
          Author = {Liauchuk, Vitali and Kovalev, Vassili},
          Keywords = {Supervoxels, Dictionary, Co-occurrence, Image Classification},
          Booktitle = {CLEF2017 Working Notes},
          Series = {{CEUR} Workshop Proceedings},
          Year = {2017},
          Publisher = { $<$$>$},
          Month = {September 11-14},
          Address = {Dublin, Ireland}


      • Serge Kozlovski <kozlovski.serge(at)>, Institute for Informatics, Minsk, Belarus
      • Vitali Liauchuk <vitali.liauchuk(at)>, Institute for Informatics, Minsk, Belarus
      • Yashin Dicente Cid <yashin.dicente(at)>, University of Warwick, Coventry, England, UK
      • Vassili Kovalev <vassili.kovalev(at)>, Institute for Informatics, Minsk, Belarus
      • Henning Müller <henning.mueller(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland