Burn Images Segmentation Based on Burn-GAN

Abstract

Introduction 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. Method 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. Results With non-saturating 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%. Conclusions 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.

Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

“…In fact, that study used an existing dataset from the first generation, and obtained similar accuracy results, despite using a more up-to-date methodology [18]. In the third generation, five studies split their data between training and validation prior to training [16,31,[33][34][35], while the four others used cross-validation (i.e., multiple random splitting of their dataset) as a validation technique [17,30,32,36].…”

“…This generation still requires that the images be pre-processed to feed the algorithm with image-specific features rather than images themselves. The third generation, introduced by teams from five different-and several new-countries, consists of studies from 2020 and 2021, which typically used transfer learning methodologies to increase the power of a small sample sizes of images, and which used as their inputs the whole images [16,17,[30][31][32][33][34][35][36]. Indeed, the specificity of transfer learning is such that the fine-tuning of the algorithm can be performed using only a few burn images on an already existing CNN, previously trained on a colossal dataset of unrelated objects for a different task [37].…”

“…Three studies looked at both identification and segmentation [23,24,29] (two of which are from the first generation [23,24]). Burn identification has remained a subject of interest across generations even though it can be considered of lesser clinical importance (if there is no burn, the clinicians do not need to be involved), with four studies from the latest generation studying this specifically [17,31,34,35]. Although not indicated clearly in the studies, burn segmentation could be useful for burn size estimation.…”

“…By contrast with the previous generation of studies [23][24][25][26], the algorithms from the second generation were entirely automatized in that a human interaction was not needed to specify where the burn injury was [18]. In the third generation, two studies used small, pre-extracted body regions of interest of defined size [30,36], as opposed to using the whole images, as in the seven others studies [16,17,[31][32][33][34][35].…”

“…Among the studies analyzed, two studies did not report any measure of overall accuracy and, rather, reported measures specific to different burn depths. They both obtained somewhat better [31] sensitivity for superficial burns, and one of them reached higher sensitivity for deep burns [35]. It is of note that the reference standard was not always specified, but when it was, diagnosis was mostly performed a posteriori using images.…”