mlsanityData to 14 Oct 2026

FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

arXiv 2305.02961 ↗First posted 4 May 2023cs.CVcited by 13

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Abstract (arXiv)

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pre-trained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on https://github.com/mrinal054/FUSegNet.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

20 of 48 references.

References · 20

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  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
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8 more
  1. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 214
  2. 2018Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blockscited by 76
  3. 2020Fully Automatic Wound Segmentation with Deep Convolutional Neural Networkscited by 32
  4. 2022FUSeg: The Foot Ulcer Segmentation Challengecited by 22
  5. 2022Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentationcited by 19
  6. 2017Fully Convolutional Networks for Diabetic Foot Ulcer Segmentationcited by 17
  7. 2021Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentationcited by 12
  8. 2021Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networkscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 6; 2026: 3.

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  1. 2023Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approachcited by 9
  2. 2024Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Studycited by 5
  3. 2025WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Carecited by 2
  4. 2025TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentationcited by 2
  5. 2024Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentationcited by 1
  6. 2025Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customizationcited by 1
  7. 2025WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patientscited by 1
  8. 2023A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rayscited by 0
  9. 2025Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imagingcited by 0
  10. 2025Robust and Annotation-Free Wound Segmentation on Noisy Real-World Pressure Ulcer Images: Towards Automated DESIGN-Rtextsuperscripttextregistered Assessmentcited by 0
  11. 2026WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentationcited by 0
  12. 2026Do Diabetic Foot Ulcer Segmentation Models Generalize? A Cross-Dataset Benchmark of CNN and Transformer Architecturescited by 0
1 more
  1. 2026WILLIE: A Unified Framework and Benchmark for Wound Classification, Segmentation, and Localizationcited by 0