Ke Cheng, Huang Yuanbo, Yang Jun, Zhang Yunjie, Zhan Huiqi, Wu Chunfa, Bi Mingye, Huang Zheng
MOE Key Laboratory of Medical Optoelectronics Science and Technology, School of Optoelectronics and Information Engineering, Fujian Normal University, Fuzhou 350100, PR China.
Department of Dermatology, Wuxi People's Hospital, Wuxi 214000, PR China.
Photodiagnosis Photodyn Ther. 2024 Apr;46:104030. doi: 10.1016/j.pdpdt.2024.104030. Epub 2024 Feb 28.
Portwine stain (PWS) birthmarks are congenital vascular malformations. The quantification of PWS area is an important step in lesion classification and treatment evaluation.
The aim of this study was to evaluate the combination of 3D scan with deep learning for automated PWS area quantization.
PWS color was measured using a portable spectrophotometer. PWS patches (29.26-45.82 cm) of different color and shape were generated for 2D and 3D PWS model. 3D images were acquired by a handheld 3D scanner to create texture maps. For semantic segmentation, an improved DeepLabV3+ network was developed for PWS lesion extraction from texture mapping of 3D images. In order to achieve accurate extraction of lesion regions, the convolutional block attention module (CBAM) and DENSE were introduced and the network was trained under Ranger optimizer. The performance of different backbone networks for PWS lesion extraction were also compared.
IDeepLabV3+ (Xception) showed the best results in PWS lesion extraction and area quantification. Its mean Intersection over Union (MIou) was 0.9797, Mean Pixel Accuracy (MPA) 0.9908, Accuracy 0.9989, Recall 0.9886 and F1-score 0.9897, respectively. In PWS area quantization, the mean value of the area error rate of this scheme was 2.61 ± 2.33.
The new 3D method developed in this study was able to achieve accurate quantification of PWS lesion area and has potentials for clinical applications.
葡萄酒色斑（PWS）胎记是先天性血管畸形。PWS面积的量化是病变分类和治疗评估的重要步骤。
本研究的目的是评估3D扫描与深度学习相结合用于自动量化PWS面积。
使用便携式分光光度计测量PWS颜色。针对二维和三维PWS模型生成不同颜色和形状的PWS斑块（29.26 - 45.82厘米）。通过手持式3D扫描仪获取三维图像以创建纹理图。对于语义分割，开发了一种改进的DeepLabV3 +网络，用于从三维图像的纹理映射中提取PWS病变。为了实现病变区域的准确提取，引入了卷积块注意力模块（CBAM）和密集连接网络（DENSE），并在Ranger优化器下对网络进行训练。还比较了不同骨干网络用于PWS病变提取的性能。
IDeepLabV3 +（Xception）在PWS病变提取和面积量化方面显示出最佳结果。其平均交并比（MIou）为0.9797，平均像素准确率（MPA）为0.9908，准确率为0.9989，召回率为0.9886，F1分数为0.9897。在PWS面积量化中，该方案的面积误差率平均值为2.61±2.33。
本研究开发的新三维方法能够实现PWS病变面积的准确量化，具有临床应用潜力。
