Skin lesion segmentation network with dual-stream discriminator based on generative adversarial networks
10.13929/j.issn.1003-3289.2024.12.023
- VernacularTitle:基于双流判别器的生成对抗皮肤病变分割网络
- Author:
Zheng AN
1
;
Le HAN
;
Ming SHI
;
Yunfei ZHOU
;
Jiahao ZHANG
Author Information
1. 山西能源学院计算机与信息工程系,山西晋中 030600
- Publication Type:Journal Article
- Keywords:
skin diseases;
neural networks,computer;
dermoscopy
- From:
Chinese Journal of Medical Imaging Technology
2024;40(12):1914-1919
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of superpixel-guided generative adversarial network with dual-stream patch-based discriminator(SPDD-PatchGAN)for segmenting skin lesions in dermatoscopy images.Methods A total of 1 279 and 10 015 dermatoscopic images of patients with skin lesions were collected from International Skin Imaging Collaboration(ISIC)2016 and Human Against Machine with 10 000 training images(HAM10000)datasets,respectively.Taken manual segmentation results as reference standards,residual attention UNet(RA-UNet)with multi-scale context extraction module(MCEM)as generator and dual stream discrimination strategy guided by superpixels based on local images as the discriminator,SPDD-PatchGAN was constructed to segment skin lesions in dermatoscopy images,and the results were compared with those of deep convolutional generative adversarial network(DCGAN),UNet,Attention-UNet,context encoder network(CENet),context pyramid fusion network(CPFNet)and generative adversarial network with dual discriminator(DAGAN).The segmenting performance of SPDD-PatchGAN was evaluated using the mean intersection over union(mIoU),Accuracy and Recall.Results The overall effect of SPDD-PatchGAN for segmenting skin lesions in dermatoscopy images was better,with mIoU,Accuracy and Recall superior to DCGAN,UNet,Attention-UNet,CENet,CPFNet and DAGAN.Conclusion SPDD-PatchGAN could effectively segment skin lesions in dermatoscopy images.
