Segmentation and Measurement of Chronic Wounds for Bioprinting.

OBJECTIVE: to provide a proof-of-concept tool for segmenting chronic wounds and transmitting the results as instructions and coordinates to a bioprinter robot and thus facilitate the treatment of chronic wounds. METHODS: several segmentation methods used for measuring wound geometry, including edge-detection and morphological operations, region-growing, Livewire, active contours, and texture segmentation, were compared on 26 images from 15 subjects. Ground-truth wound delineations were generated by a dermatologist. The wound coordinates were converted into G-code understandable by the bioprinting robot. Due to its desirable properties, alginate hydrogel was synthesized by dissolving 16% (w/v) sodium-alginate and 4% (w/v) gelatin in deionized water and used for cell encapsulation. RESULTS: Livewire achieved the best performance, with minimal user interaction: 97.08%, 99.68% 96.67%, 96.22, 98.15, and 32.26, mean values, respectively, for accuracy, sensitivity, specificity, Jaccard index, Dice similarity coefficient, and Hausdorff distance. The bioprinter robot was able to print skin cells on the surface of skin with a 95.56% similarity between the bioprinted patch's dimensions and the desired wound geometry. CONCLUSION: we have designed a novel approach for the healing of chronic wounds, based on semiautomatic segmentation of wound images, improving clinicians' control of the bioprinting process through more accurate coordinates. SIGNIFICANCE: this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.