@inproceedings{3cae639c25bd4c72904e5a20be912005,
title = "Automated 3D Wound Segmentation Using UV Based Feature Extraction and Deep Learning",
abstract = "Accurate 3D wound assessment is essential for effective clinical decision-making, but obtaining annotated wound datasets remains challenging due to privacy concerns and the labor-intensive nature of manual labeling. This study introduces a 3D wound segmentation framework that leverages simulated wound data generated via 3D scanning and advanced generative techniques. By utilizing the 2D UV-mapped texture of 3D wound surfaces, the system enables precise segmentation with deep learning methods. Specifically, we used the U-Net architecture, a widely adopted model for medical image segmentation. This proposed system offers a promising alternative to traditional 2D image and 3D volume segmentation, paving the way for improved medical imaging workflows using simulated data and multi-dimensional analysis.",
keywords = "Deep Learning, Segmentation, U-Net, Wound Image Processing, Wound Imaging",
author = "Jeffrey Jenkins and Jonathan Nguyen and Chang, \{Lin Ching\} and Salam Daher",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025 ; Conference date: 12-11-2025 Through 15-11-2025",
year = "2025",
doi = "10.1109/ICDMW69685.2025.00326",
language = "English (US)",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "2573--2576",
booktitle = "Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025",
address = "United States",
}