ImageCLEF 2021 Tuberculosis - TBT classification
Note: ImageCLEF 2021 Tuberculosis is part of the official ImageCLEF 2021 medical task. Here is a list of other ImageCLEF 2021 medical task challenges:
Note: Do not forget to read the Rules section on this page. Pressing the red Participate button leads you to a page where you have to agree with those rules. You will not be able to submit any results before agreeing with the rules.
Note: Before trying to submit results, read the Submission instructions section on this page.
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.
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.
- 1 February 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
As soon as the data is released it will be available under the "Resources" tab.
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.
We provide 3D CT image which 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; Niftilib library for C, Java, Matlab and Python and NiBabel package for Python.
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 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.
As soon as the submission is open, you will find a “Create Submission” button on this page (next to the tabs).
Before being allowed to submit your results, you have to first press the red participate button, which leads you to a page where you have to accept the challenge's rules.
А plain text file without header and with the following format:
- <CT File Name>,<TB-Type>
Please remember that according to common CLEF rules the total number of submissions is limited to 10 submissions in total (not per day).
Predictions are evaluated using accuracy and Kappa metrics.
Kappa will be used as the primary metric for ranking.
Note: In order to participate in this challenge you have to sign an End User Agreement (EUA). You will find more information on the 'Resources' tab.
ImageCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2021. CLEF 2021 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.
Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by ImageCLEF organizing committee to ensure quality. As an illustration, ImageCLEF 2020 working notes (task overviews and participant working notes) can be found within CLEF 2020 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2021. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:
Regarding the username, please choose a name that represents your team.
This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs
Participating as an individual (non affiliated) researcher
We welcome individual researchers, i.e. not affiliated to any institution, to participate. We kindly ask you to provide us with a motivation letter containing the following information:
the presentation of your most relevant research activities related to the task/tasks
your motivation for participating in the task/tasks and how you want to exploit the results
a list of the most relevant 5 publications (if applicable)
the link to your personal webpage
The motivation letter should be directly concatenated to the End User Agreement document or sent as a PDF file to bionescu at imag dot pub dot ro. The request will be analyzed by the ImageCLEF organizing committee. We reserve the right to refuse any applicants whose experience in the field is too narrow, and would therefore most likely prevent them from being able to finish the task/tasks.
Information will be posted after the challenge ends.
ImageCLEF 2021 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.
- You can ask questions related to this challenge on the Discussion Forum. Before asking a new question please make sure that question has not been asked before.
- Click on Discussion tab above or direct link: https://www.aicrowd.com/challenges/imageclef-2021-tuberculosis-tbt-classification/discussion
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 :
- contact address go here
You can find additional information on the challenge here: https://www.imageclef.org/2021/medical/tuberculosis