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）
Manu Goyal, Neil Reeves, Satyan Rajbhandari, Naseer Ahmad, Chuan Wang, Moi Hoon Yap
备注：
25 pages, 13 figures and 3 tables
发表信息：
Computers in Biology and Medicine, Volume 117, February 2020, 103616
Recognition and analysis of Diabetic Foot Ulcers (DFU) using computerized methods is an emerging research area with the evolution of image-based machine learning algorithms. Existing research using visual computerized methods mainly focuses on recognition, detection, and segmentation of the visual appearance of the DFU as well as tissue classification. According to DFU medical classification systems, the presence of infection (bacteria in the wound) and ischaemia (inadequate blood supply) has important clinical implications for DFU assessment, which are used to predict the risk of amputation. In this work, we propose a new dataset and computer vision techniques to identify the presence of infection and ischaemia in DFU. This is the first time a DFU dataset with ground truth labels of ischaemia and infection cases is introduced for research purposes. For the handcrafted machine learning approach, we propose a new feature descriptor, namely the Superpixel Color Descriptor. Then we use the Ensemble Convolutional Neural Network (CNN) model for more effective recognition of ischaemia and infection. We propose to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Finally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus non-infection. Overall, our method performed better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.
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）
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）
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）
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）
