  Acute and chronic wounds with varying etiologies burden the healthcare
systems economically. The advanced wound care market is estimated to reach $22
billion by 2024. Wound care professionals provide proper diagnosis and
treatment with heavy reliance on images and image documentation. Segmentation
of wound boundaries in images is a key component of the care and diagnosis
protocol since it is important to estimate the area of the wound and provide
quantitative measurement for the treatment. Unfortunately, this process is very
time-consuming and requires a high level of expertise. Recently automatic wound
segmentation methods based on deep learning have shown promising performance
but require large datasets for training and it is unclear which methods perform
better. To address these issues, we propose the Foot Ulcer Segmentation
challenge (FUSeg) organized in conjunction with the 2021 International
Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI). We built a wound image dataset containing 1,210 foot ulcer images
collected over 2 years from 889 patients. It is pixel-wise annotated by wound
care experts and split into a training set with 1010 images and a testing set
with 200 images for evaluation. Teams around the world developed automated
methods to predict wound segmentations on the testing set of which annotations
were kept private. The predictions were evaluated and ranked based on the
average Dice coefficient. The FUSeg challenge remains an open challenge as a
benchmark for wound segmentation after the conference.
