基于U-Net++和对抗性学习网络的乳腺肿块分割  

Breast Mass Segmentation Based on U-Net++and Adversarial Learning Network

作　　者：谢远志 闫士举[1] 魏高峰 杨林英 Xie Yuanzhi;Yan Shiju;Wei Gaofeng;Yang Linying(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Institute of Tropical Medicine,Naval Medical University,Shanghai 200025,China)

机构地区：[1]上海理工大学医疗器械与食品学院,上海200093 [2]海军军医大学热带医学研究院,上海200025

出　　处：《激光与光电子学进展》2022年第16期380-386,共7页Laser & Optoelectronics Progress

摘　　要：研究一种精确、可靠的乳腺病灶分割算法,从钼靶图像中提取肿块区域,以用于乳腺疾病的精细诊断。为了有效增强分割结果的高阶一致性,在网络框架中引入对抗网络,网络框架主要由分割网络和判别网络组成。采用改进的U-Net++网络作为分割网络,生成乳腺肿块分割图谱(掩码),而判别网络对分割产生的掩码和真实的掩码进行识别,进一步增强分割网络的性能。在公开数据集(CBIS-DDSM)上验证所提方法的有效性。实验结果显示,所提方法得到的特异性、敏感度、准确性、Dice系数分别为99.7%、90.4%、98%、91%,高于现有其他经典算法。改进模型(U-Net++)与生成对抗网络相结合的深度学习算法可提高钼靶图像中对乳腺肿块的分割性能。In this paper,an accurate and reliable breast lesion segmentation algorithm is examined to extract tumor regions from mammographic images for the diagnosis of breast diseases.Additionally,a framework incorporating an adversarial network,which is mainly composed of a segmentation network and a discriminant network,is used for the enhancement of the high-order consistency of the segmentation results.Here,an improved U-Net++network is used as the segmentation network to generate a breast mass segmentation map(a mask),while the discriminant network is used to discriminate between the generated mask and the real mask to further enhance the performance of the segmentation network.The performance of the proposed method is verified on the public dataset(CBIS-DDSM).The experimental results show that the specificity,sensitivity,accuracy,and Dice coefficient of the proposed method are 99.7%,90.4%,98%,and 91%,respectively,which are higher than that of the classical algorithms.The deep learning algorithm combined with the improved model(U-Net++)and generated countermeasure network can improve the segmentation performance of breast mass in molybdenum target images.

关 键 词：乳腺肿块分割 深度学习 U-Net++网络 对抗学习 

分 类 号：O436[机械工程—光学工程]