Photographs of 15-day wound closure progress in C57BL/6J mice

Evaluating and tracking wound size is a fundamental metric for the wound
assessment process. Good location and size estimates can enable proper
diagnosis and effective treatment. Traditionally, laboratory wound healing
studies include a collection of images at uniform time intervals
exhibiting the wounded area and the healing process in the test animal,
often a mouse. These images are then manually observed to determine key
metrics —such as wound size progress— relevant to the study. However, this
task is a time-consuming and laborious process. In addition, defining the
wound edge could be subjective and can vary from one individual to another
even among experts. Furthermore, as our understanding of the healing
process grows, so does our need to efficiently and accurately track these
key factors for high throughput (e.g., over large-scale and long- term
experiments). Thus, in this study, we develop a deep learning-based image
analysis pipeline that aims to intake non-uniform wound images and extract
relevant information such as the location of interest, wound only image
crops, and wound periphery size over-time metrics. In particular, our work
focuses on images of wounded laboratory mice that are used widely for
translationally relevant wound studies and leverages a commonly used
ring-shaped splint present in most images to predict wound size. We apply
the method to a dataset that was never meant to be quantified and, thus,
presents many visual challenges. Additionally, the data set was not meant
for training deep learning models and so is relatively small in size with
only 256 images. We compare results to that of expert measurements and
demonstrate preservation of information relevant to predicting wound
closure despite variability from machine-to-expert and even
expert-to-expert. The proposed system resulted in high fidelity results on
unseen data with minimal human intervention. Furthermore, the pipeline
estimates acceptable wound sizes when less than 50% of the images are
missing reference objects.