ImageCLEF 2018 Tuberculosis - MDR detection
Tuberculosis multi-drug-resistance detection based on CT image analysis
The ImageCLEF Tuberculosis - MDR Detection challenge has officially ended and we would like to thank everybody for their participation. You can find the official results at http://imageclef.org/2018/tuberculosis .
Post-challenge submissions and the leaderboard will remain enabled for a few weeks so you will still be able to submit result files and have them continuously evaluated during a limited period. Please consider that in order to see the version of the leaderboard with the post-challenge submissions integrated, you have to turn on the switch Show post-challenge submission right below the leaderboard.
At the same time we’d like to encourage you to submit a CLEF Working notes paper until the end of May.
Please also note that participants registering from now on will not be automatically registered with CLEF anymore.
Note: ImageCLEF Tuberculosis 2018 is divided into 3 subtasks (challenges). This challenge is about MDR (multi-drug-resistance) Detection. For information on the TBT (tuberculosis type) Classfication challenge click here . For information on the Severity Scoring challenge click here . All of these challenges share the same dataset, so registering for one of these challenges will automatically give you access to the other ones.
Note: Do not forget to read the Rules section on this page
About 130 years after the discovery of Mycobacterium tuberculosis, the disease remains a persistent threat and a leading cause of death worldwide.
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 (TBT) 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.
The goal of this challenge is to assess the probability of a TB patient having resistant form of tuberculosis based on the analysis of chest CT scan. More information will follow soon.
For this task, 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). The MDR class includes patients with extensively drug-resistant (XDR) tuberculosis.
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.
We also 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” to find the appropriate citation for this lung segmentation technique.
As soon as the submission is open, you will find a “Create Submission” button on this page (just next to the tabs)
Submit a plain text file named with the prefix MDR (e.g. MDRfree-text.txt) with the following format:
<Patient-ID>,<Probability of MDR>
Please use a score between 0 and 1 to indicate the probability of the patient having MDR.
You need to respect the following constraints:
- Patient-IDs must be part of the predefined Patient-IDs
- All patient-IDs must be present in the runfiles
- Only use numbers between 0 and 1 for the score. Use the dot (.) as a decimal point (no commas accepted)
Information will be posted after the challenge ends.
The results will be evaluated using ROC-curves produced from the probabilities provided by participants.
The leaderboard will be visible from the 01.05.2018. However, the submission system will remain open few more days.
- Technical issues : https://gitter.im/crowdAI/imageclef-2018-tuberculosis-mdr-detection
- Discussion Forum : https://www.crowdai.org/challenges/imageclef-2018-tuberculosis-mdr-detection/topics
We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at :
- Sharada Prasanna Mohanty: firstname.lastname@example.org
- Yashin Dicente Cid: yashin[DOT]dicente[AT]hevs[DOT]ch
- Henning Müller: henning[DOT]mueller[AT]hevs[DOT]ch
- Ivan Eggel: ivan[DOT]eggel[AT]hevs[DOT]ch
You can find additional information on the challenge here: http://imageclef.org/2018/tuberculosis
ImageCLEF 2018 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.