Ambiance Preservation Augmenting for Semantic Segmentation of Pediatric Burn Skin Lesions

Laura Florea, Corneliu Florea (), Constantin Vertan and Silviu Bădoiu
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Laura Florea: Image Processing and Analysis Laboratory, Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 București, Romania
Corneliu Florea: Image Processing and Analysis Laboratory, Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 București, Romania
Constantin Vertan: Image Processing and Analysis Laboratory, Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 București, Romania
Silviu Bădoiu: Department of Anatomy and Embryology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 050474 București, Romania

Mathematics, 2025, vol. 13, issue 5, 1-20

Abstract: 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.

Keywords: burn assessment; image semantic segmentation; G-Cascade; ambiance preservation; synthetic burn images (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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