Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

Abstract

Background Burns are life-threatening with high morbidity and mortality. Reliable diagnosis supported by accurate burn area and depth assessment is critical to the success of the treatment decision and, in some cases, can save the patient’s life. Current techniques such as straight-ruler method, aseptic film trimming method, and digital camera photography method are not repeatable and comparable, which lead to a great difference in the judgment of burn wounds and impede the establishment of the same evaluation criteria. Hence, in order to semi-automate the burn diagnosis process, reduce the impact of human error, and improve the accuracy of burn diagnosis, we include the deep learning technology into the diagnosis of burns. Method This article proposes a novel method employing a state-of-the-art deep learning technique to segment the burn wounds in the images. We designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network (Mask R-CNN). For training our framework, we labeled 1150 pictures with the format of the Common Objects in Context (COCO) data set and trained our model on 1000 pictures. In the evaluation, we compared the different backbone networks in our framework. These backbone networks are Residual Network-101 with Atrous Convolution in Feature Pyramid Network (R101FA), Residual Network-101 with Atrous Convolution (R101A), and InceptionV2-Residual Network with Atrous Convolution (IV2RA). Finally, we used the Dice coefficient (DC) value to assess the model accuracy. Result The R101FA backbone network gains the highest accuracy 84.51% in 150 pictures. Moreover, we chose different burn depth pictures to evaluate these three backbone networks. The R101FA backbone network gains the best segmentation effect in superficial, superficial thickness, and deep partial thickness. The R101A backbone network gains the best segmentation effect in full-thickness burn. Conclusion This deep learning framework shows excellent segmentation in burn wound and extremely robust in different burn wound depths. Moreover, this framework just needs a suitable burn wound image when analyzing the burn wound. It is more convenient and more suitable when using in clinics compared with the traditional methods. And it also contributes more to the calculation of total body surface area (TBSA) burned.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

“… 18 , 37 , 39 , 40 , 46 , 51 , 53 , 59 , 63 Further, in conditions in which there are well-established correlations between certain risk markers and an outcome of interest, such as deranged blood tests on admission and AKI in burn patients, ML yielded highly accurate predictive algorithms. 38 , 44 , 55 24 47 However, attempts to include weakly related risk markers resulted in algorithms that had an overall lower predictive accuracy, rendering them unsafe for clinical practice. This review further identified that some plastic surgery subspecialties, such as hand surgery, have yet to incorporate this technology.…”

Bullet Impact Detection in Silhouettes Using Mask R-CNN

“…Mask R-CNN is the model that has the best result, followed by the YOLOv3, MnasNet and Faster R-CNN Resnet 50 models, this can be explained because Mask R-CNN achieves good results with few images [14], unlike the others that need a dataset with more training images. Likewise, the Mask R-CNN models have better results in terms of correct detection (TP + TN) in the first than the second scenario, this can be explained because in the first scenario the models are trained with more images quantities than in the second.…”

“…However, nowadays, there are several machine learning techniques for the processing of images that have achieved high precision for a number of tasks, such as the classification of images, the detection and locating of objects, one of them is Mask R-CNN, which has achieved precisions of 97.8 %, 95.78 %, 98.5 %, and 85 % by solving problems of detection of fake images [13], detection of skin burns regions [14], detection of workers and danger zones in a building [15], and detection of breast lesions [16], respectively. So, Mask R-CNN could be used to solve DBIS.…”

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning

“…And mean intersection-over-union was increased to 0.39, but the pixel accuracy was reduced to 0.57. Jiao et al [ 16 ] designed a deep learning segmentation framework based on the mask regions with the convolutional neural network (mask R-CNN). They labelled 1150 images with the format of the Common Objects in Context (COCO) dataset and trained the model on 1000 images.…”