Rules of the HTC2022

The rules and information about the HTC2022 can also be found in this pdf . (Updated 28.10.2022)

How to enter the competition

To enter the HTC2022 competition:

Send your submission to htc2022("at")fips.fi before November 4, 2022 23:59 EET (Eastern European Time). What needs to be submitted? See below for instructions.

Only submissions that fulfill the requirements listed below will be accepted.

Requirements of the competition

What needs to be submitted? Briefly, the codes must be in Matlab or Python 3.X and the algorithms must be shared with us as a private GitHub repository at latest on deadline. Check the following subsections for detailed instructions. Only submissions that fulfill the requirements listed below will be accepted.

The teams can submit more than one reconstruction algorithm to the challenge, however, each algorithm must be in a separate repository. The maximum number of algorithms is the number of members of the team. Your team do not need to register multiple times in case you decide to submit more than one algorithm to the challenge. The team can send a single email with the links to all the repositories.

After the deadline, there is a brief period during which we can troubleshoot the codes together with the competing teams. This is to ensure that we are able to run the codes. The troubleshoot communication is done mainly via 'Issues' section of the submitted repository, so pay attention to any activities in the repository after the deadline.

Special situations: The spirit of the competition is that the algorithm is a general-purpose algorithm, capable of reconstructing limited-angle tomography images of the targets. The organizing committee has the right to disqualify an algorithm trying to violate that spirit.

Conflict of interest: researchers affiliated with the Department of Mathematics and Statistics of University of Helsinki will not be added to the leaderboard and cannot win the competition.

Deadline and what needs to be submitted:

Deadline: November 4, 2022 23:59 EET (Eastern European Time)

The algorithms must be shared with us as a private GitHub repository at latest on the deadline. The codes should be in Matlab or Python3.

After the deadline there is a brief period during which we can troubleshoot the codes together with the competing teams. This is to ensure that we are able to run the codes.

Github repository

Competitors can update the contents of the shared repository as many times as needed before the deadline. We will consider only the latest release of your repository on Github.

Attention: Simple commits to the main branch will not be considered. You MUST also create a release. You can find Github's documentation on how to create releases here. If the latest release does not work we will not accept older versions.

Your repository must contain a README.md file with at least the following sections:

Authors, institution, location.
Brief description of your algorithm and a mention of the competition.
Installation instructions, including any requirements.
- Matlab users: Please specify any toolboxes used.
- Python users: Please specify any modules used. If you use Anaconda, please add to the repository an environment.yml file capable of creating an environment than can run your code (instructions). Otherwise, please add a requirements.txt file generated with pip freeze (instructions)
Usage instructions.
Show few examples.

If your algorithm requires uploading large files to Github (e.g. with trained coefficients of a neural network), you can use Git Large File Storage (preferable way) or store them in another server and add the link to the Github installation instructions.

The teams can submit more than one algorithm to the challenge, each algorithm must be in a separate repository. The maximum number of algorithms is the number of members of the team. The teams don't need to register multiple times in case they decide to submit more than one algorithm to the challenge.

Your code on Github

The repository must contain a main routine that we can run to apply your algorithm automatically to every image in a given directory. This is the file we will run to evaluate your code. Give it an easy to identify name like main.m or main.py.

Important: The input directory contains only the test dataset. No training dataset is provided to your code during the assessment. Therefore, any training procedures must be performed by your team before the submission.

Your main routine must require three input arguments:

(string) Folder where the input image files are located
(string) Folder where the output images must be stored
(int) Difficulty category number. Values between 1 and 7

Below are the expected formats of the main routines in python and Matlab:

Matlab: The main function must be a callable function:

function main(inputFolder,outputFolder,categoryNbr)

...

your code comes here

...

Example calling the function:

>> main('path/to/input/files', 'path/to/output/files', 3)

Python: The main function must be a callable function from the command line. To achieve this you can use sys.argv or argparse module.

Example calling the function:

$ python3 main.py path/to/input/files path/to/output/files 3

The main routine must produce a reconstructed PNG file in the output folder for each image in the input folder. The output PNG images must have dimensions 512 x 512 pixels and the same filename apart from the extension. All images in the input directory belong to the same difficulty category, specified by the input argument. (Updated 28.10.2022)

The teams are allowed to use freely available python modules or Matlab toolboxes. Toolboxes, libraries and modules with paid licenses can also be used if the organizing committee also have the license. For example, the most usual Matlab toolboxes for image processing and deconvolution can be used (Image processing toolbox, wavelet toolbox, PDE toolbox, computer vision toolbox, deep learning toolbox, optimization toolbox). The teams can contact us to check if other toolboxes are available.

Scores and leaderboard

The scores and leaderboard are constructed step-wise as follows:

All teams start with difficulty level 1. The reconstructions of the three samples (A, B, and C) of this level will be assessed quantitatively following the criteria described below, and their scores will be summed, forming the total score of the first level \[ S_1=S^A_1+S^B_1+S^C_1.\] The team with the highest score $ S_1 $ will be used as reference for the cut-off score of this level: any team with score $ S_1 $ at least 25% of the highest score will pass to the next level.
The same procedure is repeated for all difficulty levels, up to level 7, but considering only the teams that passed the cut-off score of the previous level: \[ S_N=S^A_N+S^B_N+S^C_N. \] The cut-off of the levels stays fixed at 25% of the maximum at each level.
Denote by $ Nmax $ the hardest level that at least one team could enter. If there is only one team, they win. If there are several teams competing in level $ Nmax $, they are ordered in the leaderboard according to their scores at that level.
In case of a tie in level $ Nmax $, the previous levels, starting from $ Nmax-1 $, will be compared until one of the competitors win. If the tie persists, the organizing committee will make the final decision on the winner.

Note: If one team submits more than one algorithm to the competition, then each submission will be temporarily assumed to belong to different 'virtual' teams when computing the scores and cut-offs. However, this team cannot be in more than one position in the leaderboard. In this situation, the organizing committee will consider only the highest performance algorithm when ranking the winners.

Open science spirit of HTC2022

Finally, the competitors must make their GitHub repositories public at latest on November 30, 2022. In the spirit of open science, only a public code can win HTC2022.