Wound Segmentation

Wound segmentation, the automated identification of wound boundaries in images, aims to improve the efficiency and accuracy of wound care. Current research heavily utilizes deep learning, employing architectures like U-Net and variations of transformers and convolutional neural networks, often enhanced with techniques such as multi-color space merging and semi-supervised learning to address data scarcity and improve segmentation accuracy, particularly for challenging cases like dark skin tones. This work is crucial for advancing objective wound assessment, facilitating better treatment decisions, and potentially enabling personalized medicine approaches through 3D wound modeling and bioprinting. The development of large, diverse datasets and robust algorithms is driving progress in this rapidly evolving field.