Task¶

TrackRAD2025 will consist of a single task. The task will be real-time tumor tracking on time-resolved sagittal 2D cine-MRI sequences. Algorithms will be provided with a template tumor segmentation on the first frame, and the remaining 2D cine-MRI sequence which, require real-time segmentation. An example of the output the algorithms should produce (a tumor segmentation mask on each frame) can be seen in the video below:

In summary:

Inputs

• 1 cine-MRI frame with template tumor segmentation mask

• Remaining sequence of unlabelled cine-MRI frames

Outputs

• Tumor segmentation masks on the unlabelled cine-MRI frames

Attention! To enable real-time applications, your algorithm may not use information from later frames to produce the masks for previous frames.

Getting started¶

Follow these steps to set up your development environment and prepare your submission.

1. Clone the Official Repository¶

Begin by cloning the official TrackRAD2025 repository:

git clone git@github.com:LMUK-RADONC-PHYS-RES/trackrad2025.git

This repository includes:

• Challenge information
• Code used for evaluation and local testing
• A baseline algorithm to help you get started

2. Download and Place the Dataset¶

Download the dataset from Hugging Face and place it in the dataset/ folder. For unsupervised training you may want to use the unlabeled dataset, for supervised training and evaluation of your model the labeled dataset can be used.

Tip: A synthetic sequence (formatted like the labeled data) is also provided in the repository to help you get started quickly, even while still downloading the dataset.

3. Test the Baseline Algorithm¶

Before modifying anything, verify that the baseline and evaluation are working correctly:

sh test-algorithm.sh

This script will compile, execute, and test the baseline algorithm with the synthetic example scan.

Familiarize yourself with the parameters at the top of the script (especially ALGORITHM_DIR and DATASET_DIR). You will need to update these paths when evaluating your own algorithm.

4. Understand the baseline¶

Open baseline-algorithm/model.py in your code editor. You will see the following function:

def run_algorithm(frames: np.ndarray, 
    target: np.ndarray, 
    frame_rate: float, 
    magnetic_field_strength: float, 
    scanned_region: str) -> np.ndarray:
    """
    Implement your algorithm here.

    Args:
    - frames (numpy.ndarray): A 3D numpy array of shape (W, H, T) containing the MRI linac series.
    - target (numpy.ndarray): A 2D numpy array of shape (W, H, 1) containing the MRI linac target.
    - frame_rate (float): The frame rate of the MRI linac series.
    - magnetic_field_strength (float): The magnetic field strength of the MRI linac series.
    - scanned_region (str): The scanned region of the MRI linac series.
    """

    # frames.shape == (W, H, T)
    # target.shape == (W, H, 1)

    # For the example we want to repeat the initial segmentation for every frame 
    repeated_target = np.repeat(target, frames.shape[2], axis=-1)

    # repeated_target.shape == (W, H, T)
    return repeated_target

5. Start Developing Your Own Model¶

Copy the baseline algorithm folder to a new folder to work in.

cp -R baseline-algorithm your-algorithm

Navigate into your new folder and initialise it as a separate git repository:

cd your-algorithm
git init

Modify the test-algorithm.sh script in the main repository to point to your new folder (update the ALGORITHM_DIR path).

ALGORITHM_DIR="./your-algorithm"

Tip: By this time your download of the labeled dataset could be done and you could also update the dataset path, to use the labeled dataset.

# This might also be a good time to 
DATASET_DIR="./dataset/labeled"

6. Implement Your Model:¶

Edit the model.py file in your working copy to implement your algorithm. Make sure to keep the function signature of the run_algorithm function intact. This way you ensure that your submission is functional.

def run_algorithm(frames: np.ndarray, # frames.shape == (W, H, T)
    target: np.ndarray, # target.shape == (W, H, 1)
    frame_rate: float, 
    magnetic_field_strength: float, 
    scanned_region: str) -> np.ndarray:

    # Example: Repeat the initial segmentation 
    repeated_target = np.repeat(target, frames.shape[2], axis=-1)

    return repeated_target # repeated_target.shape == (W, H, T)

Note: When adding files or folders to it, make sure to add them to the Dockerfile. This is required for the submission on grand-challenge, where you provide the platform access to the github repository you created earlier. Alternatively, but not recommended, you can upload a container image, e.g. created using the save.sh script provided with the baseline algorithm. However, we still require read access to your model source code for price eligibility.

7. Have Fun¶

Have fun developing and contributing to the future of cine-MRI target tracking!

P.S.: When you are ready to test your submission on grand-challenge or submit your final version, go to the Submission Instructions.