影响因子：

1.4

DOI码：

10.1093/jbcr/iraa208

所属单位：

OXFORD UNIV PRESS

发表刊物：

JOURNAL OF BURN CARE & RESEARCH

刊物所在地：

GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND

摘要：

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 R-2 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.

合写作者：

Zhang Dengyi,Xin Ning

第一作者：

Dai Fei

论文类型：

文章

通讯作者：

Su Kehua

文献类型：

J

卷号：

42

期号：

4

页面范围：

755-762

ISSN号：

1559-047X

是否译文：

否

发表时间：

2021-09-01

收录刊物：

SCI