Facultad Ingeniería, Universidad de Deusto, Avda/Universidades 24, 48007, Bilbao, Spain.
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
Med Biol Eng Comput. 2018 Dec;56(12):2245-2258. doi: 10.1007/s11517-018-1835-y. Epub 2018 Jun 15.
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical Abstract The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.
深度学习架构的三维卷积神经网络（CNN）提供了必要的视觉特征，可准确分类和分割压疮彩色图像中的肉芽、坏死痂皮和腐肉组织。在找到感兴趣的区域（ROI）后，从原始图像和与预选的高斯核卷积的图像中提取特征，同时结合当前和先前视觉外观的一阶模型。这些模型使用离散高斯（LCDG）的线性组合来近似体素信号的经验边际概率分布。该框架在 193 张彩色压疮图像上进行了训练和测试。使用 Dice 相似系数（DSC）、面积百分比距离（PAD）和 ROC 曲线下面积（AUC）评估分类准确性和稳健性。得到的初步 DSC 为 92%、PAD 为 13%、AUC 为 95%，这是很有希望的。
摘要 基于三维卷积神经网络的压疮组织分类。
