/wound-segmentation

code and data for wound image segmentation

Primary LanguagePython

2D Wound Segmentation

This project aims at wound area segmentation from natural images in clinical settings. The architectures tested so far includes: U-Net, MobileNetV2, Mask-RCNN, SegNet, VGG16. Intro_Image Dataset_Image

Publication

Wang, C., Anisuzzaman, D.M., Williamson, V. et al. Fully automatic wound segmentation with deep convolutional neural networks. Sci Rep 10, 21897 (2020). https://doi.org/10.1038/s41598-020-78799-w

Data

The training dataset is built by our lab and collaboration clinic, Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center. With their permission, we are sharing this dataset (./data/wound_dataset/) publicly. This dataset was fully annotated by wound professionals and preprocessed with cropping and zero-padding.

Update 3/12/2021:
The dataset is now available as a MICCAI online challenge. The training and validation dataset are published here and we will start evaluating on the testing dataset in August 2021. Please find more details about the challenge here and here.

Requirements

tensorflow-gpu==1.13
Keras==2.2.4

Run

python3 train.py
python3 predict.py