Burn Images Segmentation Based on Burn-GAN
| Autor: | Ning Xin, Dengyi Zhang, Fei Dai, Kehua Su |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Image generation
Databases Factual 02 engineering and technology Regularization (mathematics) GeneralLiterature_MISCELLANEOUS 030207 dermatology & venereal diseases 03 medical and health sciences 0302 clinical medicine Sørensen–Dice coefficient Treatment plan Image Interpretation Computer-Assisted Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Medicine Computer vision Segmentation Pixel business.industry Deep learning Rehabilitation Image segmentation Emergency Medicine 020201 artificial intelligence & image processing Surgery Artificial intelligence Burns business Algorithms |
| Zdroj: | Journal of Burn Care & Research. 42:755-762 |
| ISSN: | 1559-0488 1559-047X |
| DOI: | 10.1093/jbcr/iraa208 |
| Popis: | Burn injuries are severe problems for human. Accurate segmentation for burn wounds in patient surface can improve the calculation precision of %TBSA (total burn surface area), which is helpful in determining treatment plan. Recently, deep learning methods have been used to automatically segment wounds. However, owing to the difficulty of collecting relevant images as training data, those methods cannot often achieve fine segmentation. A burn image-generating framework is proposed in this paper to generate burn image datasets with annotations automatically. Those datasets can be used to increase segmentation accuracy and save the time of annotating. This paper brings forward an advanced burn image generation framework called Burn-GAN. The framework consists of four parts: Generating burn wounds based on the mainstream Style-GAN network; Fusing wounds with human skins by Color Adjusted Seamless Cloning (CASC); Simulating real burn scene in three-dimensional space; Acquiring annotated dataset through three-dimensional and local burn coordinates transformation. Using this framework, a large variety of burn image datasets can be obtained. Finally, standard metrics like precision, Pixel Accuracy (PA) and Dice Coefficient (DC) were utilized to assess the framework. With nonsaturating loss with R2 regularization (NSLR2) and CASC, the segmentation network gains the best results. The framework achieved precision at 90.75%, PA at 96.88% and improved the DC from 84.5 to 89.3%. A burn data-generating framework have been built to improve the segmentation network, which can automatically segment burn images with higher accuracy and less time than traditional methods. |
| Databáze: | OpenAIRE |
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