mlsanityData to 7 Oct 2026

FUSeg: The Foot Ulcer Segmentation Challenge

arXiv 2201.00414 ↗First posted 2 Jan 2022eess.IV cs.CV cs.LGcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 2 Jan 2022.

Builds on

12 of 28 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  5. 2019Cascaded Partial Decoder for Fast and Accurate Salient Object Detectioncited by 216
  6. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
  7. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  8. 2020Fully Automatic Wound Segmentation with Deep Convolutional Neural Networkscited by 32
  9. 2021Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcerscited by 16
  10. 2020The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classificationcited by 14
  11. 2021Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networkscited by 9
  12. 2021Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 4; 2025: 5; 2026: 3.

21 papers.

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  1. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  2. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  3. 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Imagescited by 29
  4. 2022Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentationcited by 19
  5. 2025SAM2 for Image and Video Segmentation: A Comprehensive Surveycited by 13
  6. 2023FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentationcited by 12
  7. 2022On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustnesscited by 11
  8. 2023Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysiscited by 5
  9. 2024Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Studycited by 5
  10. 2025Wound3DAssist: A Practical Framework for 3D Wound Assessmentcited by 3
  11. 2022Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentationcited by 2
  12. 2024An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Mergingcited by 2
9 more
  1. 2025WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Carecited by 2
  2. 2025TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentationcited by 2
  3. 2024Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentationcited by 1
  4. 2024Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devicescited by 1
  5. 2025WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patientscited by 1
  6. 2023Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalizationcited by 0
  7. 2026Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentationcited by 0
  8. 2026WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentationcited by 0
  9. 2026Do Diabetic Foot Ulcer Segmentation Models Generalize? A Cross-Dataset Benchmark of CNN and Transformer Architecturescited by 0