# Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis

arXiv: https://arxiv.org/abs/2311.15836 · HTML: https://mlsanity.com/paper/2311.15836/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [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)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging](https://mlsanity.com/paper/2205.07085/) (2022)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)

## Built on by (most cited first, 5 of 5)

- [Neural Radiance Fields for the Real World: A Survey](https://mlsanity.com/paper/2501.13104/) (2025)
- [SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos](https://mlsanity.com/paper/2407.19652/) (2024)
- [Wound3DAssist: A Practical Framework for 3D Wound Assessment](https://mlsanity.com/paper/2508.17635/) (2025)
- [Multi-View Consistent Wound Segmentation With Neural Fields](https://mlsanity.com/paper/2601.16487/) (2026)
- [BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation](https://mlsanity.com/paper/2609.11463/) (2026)

## Abstract (arXiv)

Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from modern image analysis, providing accurate and precise measurements of wounds. Despite several existing techniques, the shortage of expansive and diverse training datasets remains a significant obstacle to constructing machine learning-based frameworks. This paper introduces Syn3DWound, an open-source dataset of high-fidelity simulated wounds with 2D and 3D annotations. We propose baseline methods and a benchmarking framework for automated 3D morphometry analysis and 2D/3D wound segmentation.