Ambiance Preservation Augmenting for Semantic Segmentation of Pediatric Burn Skin Lesions

Burn injuries pose a significant threat to human life, with high morbidity and mortality rates. Accurate diagnosis, including the assessment of burn area and depth, is essential for effective treatment and can sometimes be lifesaving. However, access to specialized medical professionals is often limited, particularly in remote or underserved regions. To address this challenge and alleviate the burden on healthcare providers, researchers are investigating automated diagnostic tools. The severity of the burn and the affected body surface area are critical factors in diagnosis. From a computer vision perspective, this requires semantic segmentation of burn images to assess the affected area and determine burn severity. In collaboration with medical personnel, we have gathered a dataset of in situ images from a local children’s hospital annotated by specialist burn surgeons. However, due to the limited amount of data, we propose a two-step augmentation approach: training with synthetic burn images and controlling the encoder by ambiance preservation. The latter is a technique that forces the encoder to represent closely the embeddings of images that are similar and is a key contribution of this paper. The method is evaluated on the BAMSI database, demonstrating that the proposed augmentations lead to better performance compared with strong baselines and other potential algorithmic improvements.