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One major challenge in the development of new wound healing technologies is the poor translation into the clinic. This primarily stems from differences in methodologies to assess wound healing leading to discrepancies in preclinical and clinical evaluations. The current standard of planimetric software has resulted in improved evaluation accuracy and precision in the clinical setting but has not yet been standardized for pre-clinical studies, resulting low replicability, high variability, and unknown relevance to clinical conditions. To meet these shortcomings, we seek develop a set of tools to automatically characterize wound in a variety of experimental conditions. Here, we present an application of UPSNet for the segmentation of wound images in an experimental group of pigs as a first step towards our overall goal. Our results show high overall accuracy (99.54 ± 0.28), intersection over union (93.32 ± 4.57), and boundary F1 scores (91.19 ± 8.59). Additionally, predicted wound areas differ little compared to ground truth areas (0.5308 ± 0.5501 cm2). An external dataset (n = 45) of wound images yielded high accuracy (98.64 ± 0.71), intersection over union (0.8544 ± 0.0893), and boundary F1 scores (0.6514 ± 0.1928). Predicted wound areas are comparable to ground truth areas (2.6935 ± 1.8838 cm2). With integration of additional experimental subjects, replicates, and experimental conditions, we will further develop and validate this and additional tools to automate the analysis of wound images to ultimately assess wound healing technologies.
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