FUSeg: The Foot Ulcer Segmentation Challenge

被引:7
|
作者
Wang, Chuanbo [1 ]
Mahbod, Amirreza [2 ]
Ellinger, Isabella [3 ]
Galdran, Adrian [4 ]
Gopalakrishnan, Sandeep [5 ]
Niezgoda, Jeffrey [6 ]
Yu, Zeyun [1 ]
机构
[1] Univ Wisconsin, Dept Comp Sci, Big Data Analyt & Visualizat Lab, Milwaukee, WI 53211 USA
[2] Danube Private Univ, Res Ctr Med Image Anal & Artificial Intelligence, Dept Med, A-3500 Krems, Austria
[3] Med Univ Vienna, Inst Pathophysiol & Allergy Res, A-1090 Vienna, Austria
[4] Bournemouth Univ, Dept Comp & Informat, Bournemouth BH12 5BB, England
[5] Univ Wisconsin, Coll Hlth Profess & Sci, Sch Nursing, Wound Healing & Tissue Repair Lab, Milwaukee, WI 53211 USA
[6] Adv Zenith Healthcare AZH Wound & Vasc Ctr, Milwaukee, WI 53211 USA
关键词
semantic segmentation; chronic wounds; foot ulcers; wound segmentation; challenge; benchmark;
D O I
10.3390/info15030140
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. 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). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.
引用
收藏
页数:12
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