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ImageCLEFtuberculosis

[This page is being updated...]

Welcome to the 2nd edition of the Tuberculosis Task!

Motivation

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

Description

The greatest disaster 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 using CT volumes.

Differences compared to 2017: Scoring the severity of TB cases based on chest CT images is another task compared to both tuberculosis-related subtasks considered in 2017. There are no direct links between them. Note only that original CT image datasets used in 2017 and in 2018 may slightly overlap.

News

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

Participant registration

Information will be posted soon.

Schedule

  • 08.11.2017: registration opens for all ImageCLEF tasks (until 27.04.2018)
  • 08.11.2017: development data release starts (depends on the task)
  • 20.03.2018: test data release starts (depends on the task)
  • 01.05.2018: deadline for submitting the participants runs (depends on the task)
  • 15.05.2018: release of the processed results by the task organizers (depends on the task)
  • 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

The ImageCLEFtuberculosis task includes two 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: Detection of TB Type

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

Data collection

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: Detection of TB Type

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

For both subtasks we provide 3D CT images with slice size 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.

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: Detection of TB Type

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

Citations

Information will be posted after the competition.

Organizers

  • Vassili Kovalev <vassili.kovalev(at)gmail.com>, Institute for Informatics
    Minsk, Belarus
  • Henning Müller <henning.mueller(at)hevs.ch>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
  • Vitali Liauchuk <gakarak(at)gmail.com>, Institute for Informatics
    Minsk, Belarus

Acknowledgements