ScribbleBench

ScribbleBench is a comprehensive benchmark for evaluating the generalization capabilities of 3D scribble-supervised medical image segmentation methods. It spans seven diverse datasets across multiple anatomies and modalities and provides realistic, automatically generated scribble annotations.

This repository provides:

• A guide on how to setup the ScribbleBench benchmark using the original dataset sources and our ScribbleBench scribbles.
• Our scribble generation code to create realistic interior and boundary scribbles heuristics.
• An evaluation script to evaluate your method using ScribbleBench.
• A reference to our scribble baseline nnnUNet+pL
• A scribble annotation protocol for domain experts that can be used as guidance to quickly annotate new datasets manually.

ScribbleBench was introduced in our MICCAI 2025 paper:
“Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation”
Authors: Karol Gotkowski, Klaus H. Maier-Hein, Fabian Isensee

📦 Benchmark Setup

ScribbleBench includes scribbles for the following 7 public datasets:

• ACDC
• MSCMR
• WORD
• AMOS2022 (Task2)
• KiTS2023
• LiTS
• BraTS2020

📥 Download Datasets

We provide a setup script in order to enable an easy setup of ScribbleBench that downloads and structures all datasets automatically ready for direct use. The setup script can be executed as follows:

python setup_benchmark.py \
  --dataset path/to/output \
  --mscmr --acdc [...]

If you only want to use our ScribbleBench scribbles, we provide the download link here.

🛠️ Scribble Generation

You can use our script to generate scribbles for your own 3D medical segmentation datasets. The script supports:

• Interior scribbles using NURBS curves.
• Boundary scribbles based on partial contours.
• Foreground/background slice balancing.
• Multiprocessing for efficient processing of large datasets.

🚀 Run Scribble Generation

python generate_scribbles.py \
  --input path/to/dense_segmentations \
  --output path/to/save_scribbles \
  --num_labels 4 \
  --conf scribble_conf.yml \
  --processes 8

Optional arguments:

• --name → specify one or more file names to process (omit .nii.gz)
• --disable_ignore → disables marking unlabeled voxels with an ignore label

📊 Evaluation

You can evaluate your segmentation predictions using the provided script:

python evaluation.py \
  --gt_dir path/to/ground_truth \
  --pred_dir path/to/predictions \
  --num_labels 4 \
  --processes 8

🧠 Scribble Baseline nnUNet+pL

Our scribble baseline nnUNet+pL is implemented in the nnU-Net framework itself. It is there referred to as "ignore label" and is described here.

📋 Scribble Annotation Protocol

You can also manually create your own scribbles for new datasets by following this lightweight annotation protocol. These human-created scribbles can be used directly to train a model using the same methods as with automatically generated ones.

✏️ Instructions

Given a 3D image I in your dataset:

• For each axial slice S in I:
  • For each class C present in slice S:
    • Select a single connected component (CC) of class C in S
    • For that component CC, draw:
      • One interior scribble
      • One boundary scribble

Note: Do not ignore the background class! Also include a good number of pure background slices.

🟢 Interior Scribble

• Must be drawn inside the component CC.
• Should be placed roughly in and around the center area of the component.
• Ideal length is comparable to the diameter or extent of the component.
• Can be any arbitrary shape (straight, curved, etc.) as long as it lies fully within the component.

🔵 Boundary Scribble

• Should trace a portion (15%–100%) of the inner boundary of the component CC.
• Should ideally follow the actual boundary as closely as possible.
• A 1–3 voxel inward offset is acceptable, but closer to the true boundary is better.
• This scribble helps the model capture boundary details during learning.

Following this protocol allows quick and efficient labeling of 3D datasets using just a few sparse lines per class and slice, while maintaining strong training performance.

📄 Citation

If you use ScribbleBench or our scribble generation code, please cite:

@inproceedings{gotkowski2025scribblebench,
  title     = {Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation},
  author    = {Karol Gotkowski and Klaus H. Maier-Hein and Fabian Isensee},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2025}
}

📬 Contact

For questions, suggestions, or contributions, feel free to open an issue or contact karol.gotkowski@dkfz.de.

Acknowledgments

<p align="left"> <img src="https://github.com/MIC-DKFZ/vidata/raw/main/imgs/Logos/HI_Logo.png" width="150"> &nbsp;&nbsp;&nbsp;&nbsp; <img src="https://github.com/MIC-DKFZ/vidata/raw/main/imgs/Logos/DKFZ_Logo.png" width="500"> </p>

This repository is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at DKFZ.