# Wound3DAssist: A Practical Framework for 3D Wound Assessment

arXiv: https://arxiv.org/abs/2508.17635 · HTML: https://mlsanity.com/paper/2508.17635/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (2020)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis](https://mlsanity.com/paper/2311.15836/) (2023)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)
- [SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos](https://mlsanity.com/paper/2407.19652/) (2024)
- [ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization](https://mlsanity.com/paper/2209.12822/) (2022)
- [A graph-based mathematical morphology reader](https://mlsanity.com/paper/1404.7748/) (2014)
- [Stability of Weighted Majority Voting under Estimated Weights](https://mlsanity.com/paper/2207.06118/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multi-View Consistent Wound Segmentation With Neural Fields](https://mlsanity.com/paper/2601.16487/) (2026)
- [Non-Invasive 3D Wound Measurement with RGB-D Imaging](https://mlsanity.com/paper/2601.19014/) (2026)
- [AI-Driven Three-Dimensional Reconstruction and Quantitative Analysis for Burn Injury Assessment](https://mlsanity.com/paper/2602.00113/) (2026)

## Abstract (arXiv)

Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks have aided wound measurement, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates 3D wound models from short handheld recordings captured using consumer-grade devices, enabling non-contact, automatic measurements from reconstructed multi-view surfaces. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, approximately millimeter-scale surface reconstruction accuracy in the evaluated clinical cases, and multi-view wound-tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for a research framework intended for future clinical workflow evaluation.