mlsanityData to 14 Oct 2026

Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation

arXiv 2204.11618 ↗First posted 22 Apr 2022eess.IV cs.CVcited by 19

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Abstract (arXiv)

Diabetic foot ulcer is a severe condition that requires close monitoring and management. For training machine learning methods to auto-delineate the ulcer, clinical staff must provide ground truth annotations. In this paper, we propose a new diabetic foot ulcers dataset, namely DFUC2022, the largest segmentation dataset where ulcer regions were manually delineated by clinicians. We assess whether the clinical delineations are machine interpretable by deep learning networks or if image processing refined contour should be used. By providing benchmark results using a selection of popular deep learning algorithms, we draw new insights into the limitations of DFU wound delineation and report on the associated issues. This paper provides some observations on baseline models to facilitate DFUC2022 Challenge in conjunction with MICCAI 2022. The leaderboard will be ranked by Dice score, where the best FCN-based method is 0.5708 and DeepLabv3+ achieved the best score of 0.6277. This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results. DFUC2022 will be released on the 27th April 2022.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

18 of 41 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  7. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  8. 2016Language Modeling with Gated Convolutional Networkscited by 702
  9. 2020Fully Automatic Wound Segmentation with Deep Convolutional Neural Networkscited by 32
  10. 2022FUSeg: The Foot Ulcer Segmentation Challengecited by 22
  11. 2020Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluationcited by 19
  12. 2017Fully Convolutional Networks for Diabetic Foot Ulcer Segmentationcited by 17
6 more
  1. 2021Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcerscited by 16
  2. 2021Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networkscited by 9
  3. 2021Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summarycited by 8
  4. 2021A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcerscited by 7
  5. 2017Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networkscited by 6
  6. 2022Development of Diabetic Foot Ulcer Datasets: An Overviewcited by 3

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Citing papers per year: 2022: 2; 2023: 6; 2024: 5; 2025: 6.

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  1. 2023Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Modelscited by 28
  2. 2023FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentationcited by 13
  3. 2022On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustnesscited by 11
  4. 2024VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blockscited by 11
  5. 2022HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentationcited by 6
  6. 2023Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysiscited by 5
  7. 2024Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Studycited by 5
  8. 2024TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Modelscited by 3
  9. 2025Wound3DAssist: A Practical Framework for 3D Wound Assessmentcited by 3
  10. 2024An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Mergingcited by 2
  11. 2025Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Datasetcited by 2
  12. 2025WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Carecited by 2
7 more
  1. 2023Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classificationcited by 1
  2. 2024Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devicescited by 1
  3. 2025Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customizationcited by 1
  4. 2025MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidationcited by 1
  5. 2023Diabetic Foot Ulcer Grand Challenge 2022 Summarycited by 0
  6. 2023ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Modelscited by 0
  7. 2025Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentationcited by 0