Lesion segmentation using 3D scan and deep learning for the evaluation of facial portwine stain birthmarks

Background: Portwine stain (PWS) birthmarks are congenital vascular malformations. The quantification of PWS area is an important step in lesion classification and treatment evaluation. Aims: The aim of this study was to evaluate the combination of 3D scan with deep learning for automated PWS area quantization. Materials and methods: PWS color was measured using a portable spectrophotometer. PWS patches (29.26-45.82 cm2) 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. Results: 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. Conclusions: The new 3D method developed in this study was able to achieve accurate quantification of PWS lesion area and has potentials for clinical applications.