Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not managed properly can lead to amputation. DFU can appear anywhere on the foot and can vary in size, colour, and contrast depending on various pathologies. Current clinical approaches to DFU treatment rely on patients and clinician vigilance, which has significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of 705 foot images. We provide the ground truth of ulcer region and the surrounding skin that is an important indicator for clinicians to assess the progress of ulcer. Then, we propose a two-tier transfer learning from bigger datasets to train the Fully Convolutional Networks (FCNs) to automatically segment the ulcer and surrounding skin. Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0.794 ($\pm$0.104) for ulcer region, 0.851 ($\pm$0.148) for surrounding skin region, and 0.899 ($\pm$0.072) for the combination of both regions. This demonstrates the potential of FCNs in DFU segmentation, which can be further improved with a larger dataset.
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生局）；University of Southern California（南加利福尼亚大学）
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers
Faculty of Science and Engineering, Manchester Metropolitan University（曼彻斯特城市大学科学与工程学院）；Lanchashire Teaching Hospitals（兰开夏教学医院）；Waikato District Health Board（怀卡托地区卫生局）
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）；United Lincolnshire Hospitals NHS Trust（联合林肯郡医院NHS信托）；Jersey General Hospital（泽西总医院）；Eastbourne District General Hospital（伊斯特本地区综合医院）
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospital（兰开夏教学医院）；Waikato District Health Board（怀卡托地区卫生委员会）；Manipal College of Health Professions（马尼帕尔健康专业学院）；University of Waikato（怀卡托大学）；University of Manchester（曼彻斯特大学）；University of Southern California（南加州大学）；Baylor College of Medicine（贝勒医学院）
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospital（兰开夏教学医院）；University of Manchester（曼彻斯特大学）；Manchester Royal Infirmary（曼彻斯特皇家医院）；Sun Yat-sen University（中山大学）
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Manchester Metropolitan University（曼彻斯特城市大学）；University of Manchester（曼彻斯特大学）；Lancashire Teaching Hospital（兰开夏教学医院）；Lancashire Care NHS Foundation Trust（兰开夏护理NHS基金会信托）
A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；The University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学NHS基金会信托）；Salford Royal NHS Foundation Trust（索尔福德皇家NHS基金会信托）
A quantitative comparison of plantar soft tissue strainability distribution and homogeneity between ulcerated and non-ulcerated patients using strain elastography
Staffordshire University（斯塔福德郡大学）；AR Diabetes Hospitals（AR糖尿病医院）；Technische Universität München（慕尼黑工业大学）
UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
KITE Research Institute（凯特研究所）；University Health Network（大学健康网络）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University of Toronto Mississauga（多伦多大学密西沙加分校）
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
Manchester Metropolitan University（曼彻斯特城市大学）；Keio University（庆应义塾大学）；Baker Heart and Diabetes Institute（贝克心脏与糖尿病研究所）；University of Applied Sciences and Arts Dortmund (FH Dortmund)（多特蒙德应用科学与艺术大学）；Dartmouth College（达特茅斯学院）；Shanghai University（上海大学）
Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers
University of Applied Sciences and Arts Dortmund (FH Dortmund)（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）
ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification
Department of Information Technology（信息科技系）；Charles Darwin University（查尔斯·达尔文大学）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Liberal Arts Bangladesh（孟加拉自由大学）；BRAC University（BRAC大学）；Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET)（高级分析中心（CAA）、人工智能学院、工程与技术学院）；Multimedia University Melaka（马六甲多媒体大学）；American International University-Bangladesh（美国国际大学-孟加拉）
Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；KITE, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE部门）
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University Health Network（多伦多大学健康网络）；Zivot Limb Preservation Centre, Peter Lougheed Centre（齐沃特肢体保护中心（彼得·劳埃德中心））；Cumming School of Medicine, University of Calgary（卡尔加里大学卡明医学院）
WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring
The Catholic University of America（美国天主教大学）；Center for Advanced Research in Computer Engineering（计算机工程高级研究中心）
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；Institute for Pathophysiology and Allergy Research, Medical University of Vienna（维也纳医科大学病理生理学与过敏研究所）；Bournemouth University（伯恩茅斯大学）；Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center（AZH伤口与血管中心）
Robust and Annotation-Free Wound Segmentation on Noisy Real-World Pressure Ulcer Images: Towards Automated DESIGN-R\textsuperscript{\textregistered} Assessment
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology（密苏里科技大学机械与航空航天工程系）；Intelligent System Center, Missouri University of Science and Technology（密苏里科技大学智能系统中心）；Center for Biomedical Research, Missouri University of Science and Technology（密苏里科技大学生物医学研究中心）；Wound Care Department, Charles George Department of Veterans Affairs Medical Center（查尔斯·乔治退伍军人事务医疗中心伤口护理部）
Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；Advancing the Zenith of Healthcare (AZH) Wound and Vascular Center（医疗保健提升巅峰伤口与血管中心）；College of Nursing, University of Wisconsin Milwaukee（威斯康星大学密尔沃基分校护理学院）
