This Zenodo upload contains the best performing segmentation dockers from the TopBrain 2025 challenge, along with instructions and scripts to help you run them locally. The dockers are from the top-3 teams for CTA and MRA modalities: team ARG, KDH, and UZH. The docker files are named as Team_{team-name}_{year}_topbrain_segmentation_{modality}.tar.gz The `modality` in the docker file name indicates whether the docker is for CT or MR angiographies. The docker from team UZH work for both CTA and MRA modalities. How to Run Predictions Yourself Pre-requisite for the Input Images: LPS+ The only pre-requisite for the images is orientation. The image MUST be in LPS+ orientation. We provide a Python script `reorient_nii.py` that you can directly use to re-orient your input images to LPS+. python3 reorient_nii.py LPS The input image to the dockers can be images of these types: "*.nii.gz", "*.nii", and "*.mha". Optional crop to braincase region: For best results and also to reduce memory, you can crop the input images to the braincase region. Docker Load Image and `run_docker_topbrain_2025.py` Once you have downloaded a team's docker, first load the docker image with `docker image load -i`: docker image load -i Then you need to note down the loaded docker image's REPOSITORY:TAG from `docker images`. For example, when you run: $ docker images REPOSITORY TAG IMAGE ID CREATED SIZE topbrain-ct v2.0 a8961a3dd01c 7 months ago 9.59GB In the above example, the REPOSITORY:TAG will be `topbrain-ct:v2.0`. You need to know the repo:tag pair to run the docker container below. With the folder containing your input images to be predicted, the modality, and the noted repo:tag, you can simply run the provided Python script `run_docker_topbrain_2025.py` as follows: python3 run_docker_topbrain_2025.py \ --img_src \ --modality \ --repo_tag The predictions are saved in the folder Saved_predictions__.