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Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks

arXiv 2010.05855 ↗First posted 12 Oct 2020eess.IV cs.CVcited by 32

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Abstract (arXiv)

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed 22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. Particularly, MobileNetV2 stands out among others due to its lightweight architecture and uncompromised performance. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. We build an annotated wound image dataset consisting of 1,109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 7; 2023: 5; 2024: 4; 2025: 9; 2026: 5.

32 papers.

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  1. 2022FUSeg: The Foot Ulcer Segmentation Challengecited by 21
  2. 2022Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentationcited by 19
  3. 2021Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentationcited by 12
  4. 2023FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentationcited by 12
  5. 2021Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networkscited by 9
  6. 2024Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimescited by 8
  7. 2023Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysiscited by 5
  8. 2024Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Studycited by 5
  9. 2023DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Imagescited by 4
  10. 2022Development of Diabetic Foot Ulcer Datasets: An Overviewcited by 3
  11. 2022ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorizationcited by 3
  12. 2025Wound3DAssist: A Practical Framework for 3D Wound Assessmentcited by 3
20 more
  1. 2022Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentationcited by 2
  2. 2023gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentationcited by 2
  3. 2024An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Mergingcited by 2
  4. 2025Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Datasetcited by 2
  5. 2025WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Carecited by 2
  6. 2025TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentationcited by 2
  7. 2024Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devicescited by 1
  8. 2025Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customizationcited by 1
  9. 2025WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patientscited by 1
  10. 2022Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imagingcited by 0
  11. 2022Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcerscited by 0
  12. 2023Diabetic Foot Ulcer Grand Challenge 2022 Summarycited by 0
  13. 2025Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imagingcited by 0
  14. 2025Robust and Annotation-Free Wound Segmentation on Noisy Real-World Pressure Ulcer Images: Towards Automated DESIGN-Rtextsuperscripttextregistered Assessmentcited by 0
  15. 2025WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoringcited by 0
  16. 2026Multi-View Consistent Wound Segmentation With Neural Fieldscited by 0
  17. 2026Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound imagescited by 0
  18. 2026Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoringcited by 0
  19. 2026In-Context Learning for Wound Classification with Small Multimodal Language Modelscited by 0
  20. 2026Synthetic Leprosy Image Generation Using Mask-Conditioned Latent Diffusion and Transfer Learning from Large Chronic Wound Datasetscited by 0