This repository contains the official implementation of the paper:
"WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care",
which was accepted for presentation in the Applied Data Science Track at ECML PKDD 2025.
If you find this repository useful for your research or work, please cite our contribution as follows:
@inproceedings{borst2025woundambit,
title = {WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care},
author = {Borst, Vanessa and Dittus, Timo and Dege, Tassilo and Schmieder, Astrid and Kounev, Samuel},
booktitle = {Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track},
publisher = {Springer Nature Switzerland},
year = {2025}
}Dependencies can be installed with the following commands, using an anaconda or miniconda environment
conda create -n "woundambit" python=3.11
conda activate woundambit
pip3 install -e ./src/main
pip3 install -r requirements.txt
If a GPU is available, set the CUDA_VISIBLE_DEVICES environment variable to the GPU ID before running the code:
export CUDA_VISIBLE_DEVICES=<GPU_ID>For the usage of FUSegNet, TransNeXt, and InternImage, please look at the model-specific installation remarks.
The framework uses pretrained ImageNet weights for all models, which need to be downloaded manually in advance for most architectures. For more details regarding the required ImageNet weights, please refer to the respective documentation.
For the architectures used in our publication, pretrained weights that are not downloaded automatically are already included under
data/pretrained.
The trained 5-fold wound segmentation models are available on Zenodo.
To use the below tools for inference on custom images, benchmarking or size retrieval, please download the trained models,
unpack them, and place the k_fold_models folder in the ./out folder.
Different tools that use the trained model checkpoints can be found in ./src/main/medseg/tools.
In addition to the code for retrieving and evaluating wound sizes, there are tools for benchmarking
different models and for performing inference with the trained AI models on custom images.
Please refer to the dedicated tools documentation for more information.
Datasets have to be downloaded manually and in case of the DFUC dataset require a registration.
However, converter scripts for the supported datasets can be found in src/main/medseg/data/converters.
The scripts are adapted to the folder structure of the downloaded dataset and should require no additional
configuration. Please refer to the documentation in the respective converter script for more information.
Example:
python3 ./src/main/medseg/data/converters/convert_cfu.py --in_path_dfuc="/home/user/Downloads/DFUC 2022 Data" --in_path_fuseg="/home/user/Downloads/FUSeg 2021 Data" --out_path="./data/datasets/cfu"
The out_path argument should be set to the default path used in the respective dataset definition in src/main/medseg/data/datasets for the framework to find them without additional configuration.
The out-of-distribution (OOD) dataset used in the publication to assess model generalizabilty can not be shared
completely due to privacy regulations.
However, with the requisite written consent, selected examples of the OOD dataset, along with all 20 images utilized for
the purpose of evaluating the size retrieval performance, can be made available.
These can be found in the directories labelled ./data/datasets/ood and ./data/datasets/size_retrieval, respectively.
📝 : Within the code, the OOD dataset is referred to as UkwTest.
The configs that were used for creating the publication results can be found in ./configs and
include details on the model architectures, training parameters, and more. To make them work, the specified datasets
must exist (see above section for how to create them).
For details regarding the training of new models and the subsequent evaluation, please refer to the custom training and evaluation documentation.
Additional files such as supplementary analysis, archived code snapshot, and trained model weights are hosted on Zenodo:
This repository is licensed under the MIT License for all original code provided by the authors.
However, portions of the codebase incorporate or adapt components from other open-source projects with differing licenses (e.g., Apache 2.0, NVIDIA Source Code License). These third-party modules retain their original licenses.
Please make sure to consult the respective licenses if you intend to reuse or redistribute portions of the included third-party code. For details, see the Third-Party License Attribution section below.
This project includes third-party components under various licenses (e.g., Apache 2.0, MIT, NVIDIA Source Code License).
The list below contains only a selection of the major models.
For each implementation, external code is explicitly attributed to the original authors, with corresponding license
information provided in the header of the respective source files located in src/main/medseg/models/.
| Model/Component | Authors/Copyright | License |
|---|---|---|
| ConvNeXt | OpenMMLab/MMPreTrain Authors | Apache 2.0 |
| FCBFormer | Edward Sanderson and Bogdan J. Matuszewski | Apache 2.0 |
| FUSegNet | Pavel Iakubovskii and Mrinal Kanti Dhar (?) | MIT |
| HarDNet-DFUS | ? | Not specified |
| HiFormer | Amirhossein Kazerouni | MIT |
| InternImage | OpenGVLab | MIT |
| M3d-Cam (GradCAM) | Karol Gotkowski | MIT |
| MISSformer | ? | Not specified |
| PVT | Facebook, Inc | Apache 2.0 |
| SegFormer | NVIDIA Corporation | NVIDIA Source Code License |
| SegNeXt | SegNeXt Authors | Apache 2.0 |
| TransNeXt | Dai Shi | Apache 2.0 |
| UPerNet | OpenMMLab/MMPreTrain Authors | Apache 2.0 |
| VWFormer | Haotian Yan | MIT |
❗Please refer to the license headers within individual source files and the original repositories for more detailed licensing information❗
The patient images and trained AI models included in this repository are provided for research and educational purposes only.
- Attribution Required: Users must cite the original paper when using these resources.
- Redistribution & Commercial Use: Not permitted without explicit permission from the authors.
- Ethical Compliance: Using patient data requires compliance with all applicable privacy,ethical and legal regulations.
For full details, see the DATA_LICENSE.md.
This repository builds upon a codebase developed by Timo Dittus during his Master's thesis project at the University of Würzburg in 2023, under the supervision of Vanessa Borst at the Chair of Computer Science II, led by Prof. Dr. Samuel Kounev.
Afterwards, the framework has been substantially modified and extended by Vanessa Borst. Extensions include the addition of new segmentation models, integration of the out-of-distribution (OOD) dataset, development of tools for benchmarking and inference, and the design of the wound size retrieval system.
See Contributors.md for a full list of contributors and their respective roles.
In case you have any questions or want to collaborate, feel free to contact me at:
- Mail: vanessa.borst@uni-wuerzburg.de
- LinkedIn: Vanessa Borst