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Welcome to the 2nd edition of the Tuberculosis Task!


About 130 years after the discovery of Mycobacterium tuberculosis, the disease remains a persistent threat and a leading cause of death worldwide.


The greatest problem that can happen to a patient with tuberculosis (TB) is that the organisms become resistant to two or more of the standard drugs. In contrast to drug sensitive (DS) tuberculosis, its multi-drug resistant (MDR) form is much more difficult and expensive to recover from. Thus, early detection of the drug resistance (DR) status is of great importance for effective treatment. The most commonly used methods of DR detection are either expensive or take too much time (up to several month). Therefore, there is a need for quick and at the same time cheap methods of DR detection. One of the possible approaches for this task is based on Computed Tomography (CT) image analysis. Another challenging task is automatic detection of TB types (TBT) using CT volumes. In this subtask, five types of tuberculosis are considered: Infiltrative, Focal, Tuberculoma, Miliary and Fibro-cavernous. Lung lesions have different appearance, size and pattern depending on the TB type.

Differences compared to 2017:

  • Both training and test datasets for MDR recognition task (subtask #1) will be extended by means of adding several cases with extensively drug-resistant tuberculosis (XDR TB), which is a rare and more severe subtype of MDR TB.
  • In case of TB type detection (subtask #2) the datasets will be extended by adding new CT scans of the same patients involved in 2017, and also by introducing CT images of a few new patients.
  • A new task (subtask #3) compared to 2017 is introduced which is dedicated to scoring of severity of TB cases based on chest CT images.


  • 18.10.2017:first information on the task being available on the web pages

Participant registration

Please refer to the general ImageCLEF registration instructions


  • 08.11.2017: registration opens for all ImageCLEF tasks (until 27.04.2018)
  • 15.01.2018: development data release starts
  • 20.03.2018: test data release starts
  • 01.05.2018: deadline for submitting the participants runs
  • 15.05.2018: release of the processed results by the task organizers
  • 31.05.2018: deadline for submission of working notes papers by the participants
  • 15.06.2018: notification of acceptance of the working notes papers
  • 29.06.2018: camera ready working notes papers
  • 10-14.09.2018: CLEF 2018, Avignon, France

Subtasks Overview

[This section is still under construction]

The ImageCLEFtuberculosis task 2018 includes three independent subtasks.

Subtask #1: MDR detection

The goal of this subtask is to assess the probability of a TB patient having resistant form of tuberculosis based on the analysis of chest CT scan.

Subtask #2: TBT classification

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.

Subtask #3: Severity scoring

This subtask is aimed at assessing TB severity score based on chest CT image. The Severity score is a cumulative score of severity of TB case assigned by a medical doctor. Originally, the score varied from 1 ("critical/very bad") to 5 ("very good"). In this subtask, the score value is simplified so that values 1, 2 and 3 correspond to "high severity" class, and values 4 and 5 correspond to "low severity". In the process of scoring, the medical doctors considered many factors like pattern of lesions, results of microbiological tests, duration of treatment, patient's age and some other. The goal of this subtask is to distinguish "low severity" from "high severity" based on the CT image, only.

Data collection

[This section is still under construction]

Subtask #1: MDR detection

For subtask #1, a dataset of 3D CT images is used along with a set of clinically relevant metadata. The dataset includes only HIV-negative patients with no relapses and having one of the two forms of tuberculosis: drug sensitive (DS) or multi-drug resistant (MDR).

# Patients Train Test
DS 134 101
MDR 96 113
Total patients 230 214

Subtask #2: TBT classification

The dataset used in subtask #2 includes chest CT scans of TB patients along with the TB type.

# Patients Train Test
Type 1 140 80
Type 2 120 70
Type 3 100 60
Type 4 80 50
Type 5 60 40
Total patients 500 300

Subtask #3: Severity scoring

The dataset for subtask #3 includes chest CT scans of TB patients along with the corresponding severity level designated as "low" and "high". The total number of patients for testing is not determined yet. The training set it is already fixed.

# Patients Train Test
Low severity 90 ~55
High severity 80 ~40
Total patients 170 ~95

For all subtasks we provide 3D CT images with an image size per slice of 512*512 pixels and number of slices varying from about 50 to 400. 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 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.

Moreover, for all patients in both subtasks we provide automatic extracted masks of the lungs. This material can be downloaded together with the patients CT images. The details of this segmentation can be found here.
In case the participants use these masks in their experiments, please refer to the section "Citations" at the end of this page to find the appropriate citation for this lung segmentation technique.

Remarks on the automatic lung segmentation:

The segmentations were manually analysed based on statistics on number of lungs found and size ratio of the lungs. Only those segmentations with anomalies on these statistics were visualized. The code used to segment the patients was improved considering the cases wrong segmented. After all improvements, only one segmentation remained wrong. The final segmentation provided in the dataset is the result of a supervised fusion of the above mentioned method and a registration-based segmentation. The patient affected is the SVG_TRN_154 (Subtask 3, training set, patient 154).

Submission instructions

Information will be posted closer to the submission deadline.

Evaluation methodology

Subtask #1: MDR detection

The results will be evaluated using ROC-curves produced from the probabilities provided by participants.

Subtask #2: TBT classification

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

Subtask #3: Severity scoring

The results will be evaluated considering this subtask as a classification problem and as a regression problem. Measures such as accuracy, unweighted Cohen’s Kappa, and mean square error will be used.


More information will be posted after the competition.

  • When using the provided mask of the lungs , please cite the following publication:
    • 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 = {CEUR-WS},
        Location = {New York, USA}


  • Organizers

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