U-Net-based DFU Tissue Segmentation and Registration on Uncontrolled Dermoscopic Images

Yanexis Toledo, Leandro A. F. Fernandes, Silena Herold-Garcia, Alexis P. Quesada

2022

Abstract

Diabetic Foot Ulcers (DFUs) are aggressive wounds with high morbimortality due to their slow healing capacity and rapid tissue degeneration, which cause complications such as infection, gangrene, and amputation. The automatic analysis of the evolution of tissues associated with DFU allows the quick identification and treatment of possible complications. In this paper, our contribution is twofold. First, we present a new DFU dataset composed of 222 images labeled by specialists. The images followed the healing process of patients of an experimental treatment and were captured under uncontrolled viewpoint and illumination conditions. To the best of our knowledge, this is the first DFU dataset whose images include the identification of background and six different classes of tissues. The second contribution is an U-Net-based segmentation and registration procedure that uses features computed by hidden layers of the network and epipolar constraints to identify pixelwise correspondences between images of the same patient at different healing stages.

Paper Citation

in Harvard Style

Toledo Y., Fernandes L., Herold-Garcia S. and Quesada A. (2022). U-Net-based DFU Tissue Segmentation and Registration on Uncontrolled Dermoscopic Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 510-517. DOI: 10.5220/0010868600003124

in Bibtex Style

@conference{visapp22,
author={Yanexis Toledo and Leandro A. F. Fernandes and Silena Herold-Garcia and Alexis P. Quesada},
title={U-Net-based DFU Tissue Segmentation and Registration on Uncontrolled Dermoscopic Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={510-517},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010868600003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - U-Net-based DFU Tissue Segmentation and Registration on Uncontrolled Dermoscopic Images
SN - 978-989-758-555-5
AU - Toledo Y.
AU - Fernandes L.
AU - Herold-Garcia S.
AU - Quesada A.
PY - 2022
SP - 510
EP - 517
DO - 10.5220/0010868600003124
PB - SciTePress