A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks

Annu Int Conf IEEE Eng Med Biol Soc. 2015:2015:2415-8. doi: 10.1109/EMBC.2015.7318881.


Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Automation
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning
  • Neural Networks, Computer*
  • Reproducibility of Results
  • Wound Healing*