ImageCLEF 2019 Tuberculosis - CT report

Note: ImageCLEF Tuberculosis 2019 is divided into 2 subtasks (challenges). This challenge is about CT report. For information on the Severity Scoring challenge click here . Both challenges share the same dataset, so registering for one of these challenges will automatically give you access to the other one.

Note: Do not forget to read the Rules section on this page.

Motivation

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 the evaluation of the severity stage are two important tasks.

Challenge description

In this subtasks the participants will have to generate an automatic report based on the CT image. This report should include the following information in binary form (0 or 1): Left lung affected, right lung affected, presence of calcifications, presence of caverns, pleurisy, lung capacity decrease.

Data

In this edition, both subtasks (SVR and CTR) use the same dataset containing 335 chest CT scans of TB patients along with a set of clinically relevant metadata. 218 patients are used for training and 117 for test. The selected metadata includes the following binary measures: disability, relapse, symptoms of TB, comorbidity, bacillary, drug resistance, higher education, ex-prisoner, alcoholic, smoking.

For all patients 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” in the ImageCLEF TB 2019 website 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 between right-left lung. Only those segmentations with anomalies on these statistics were visualized. The code used to segment the patients was adapted for the cases with unsatisfactory segmentation. After this proceeding, all patients with anomalies presented a satisfactory mask.

Submission instructions

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 CTR (e.g. CTRfree-text.txt) with the following format:

<Patient-ID>,<Probability of “left lung affected”>,<Probability of “right lung affected”>,<Probability of “presence of calcifications”>,<Probability of “presence of caverns”>,<Probability of “pleurisy”>,<Probability of “lung capacity decrease”>

e.g.:

CTR_TST_001,0.93,0.2,0.655,0.01,0.3645,0.98
CTR_TST_002,0.54,0,1,0.25,0.2,0.598,0
CTR_TST_003,0.1,0.50,0.0,1.0,0.999,0.46
CTR_TST_004,0.245,0.12,0.23,0.34,0.45,0.68
CTR_TST_005,0.7,0.1,0,0,0,0

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 probabilities. Use the dot (.) as a decimal point (no commas accepted)

Citations

Information will be posted after the challenge ends.

Evaluation criteria

This task is considered a multi-binary classification problem (6 binary findings). Measures including AUC and accuracy will be used to evaluate the task. The ranking of this task will be done first by average AUC and then by min AUC (both over the 6 CT findings).

Resources

Contact us

• Discussion Forum : https://www.crowdai.org/challenges/imageclef-2019-tuberculosis-ct-report/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 :

Yashin Dicente Cid <yashin.dicente(at)hevs.ch>, University of Applied Sciences Western Switzerland, Sierre, Switzerland Vitali Liauchuk <vitali.liauchuk(at)gmail.com>, Institute for Informatics, Minsk, Belarus 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

More information

You can find additional information on the challenge here: https://www.imageclef.org/2019/medical/tuberculosis

Prizes

ImageCLEF 2019 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.

Datasets License