基于同构化改进的U-Net结直肠息肉分割方法  

A Colorectal Segmentation Method Based on U-Net Improved with Identical Design

作　　者：沈志强 林超男 潘林[1] 聂炜宇 裴玥 黄立勤[1] 郑绍华[1] Shen Zhiqiang;Lin Chaonan;Pan Lin;Nie Weiyu;Pei Yue;Huang Liqin;Zheng Shaohua(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,Fujian,China)

机构地区：[1]福州大学物理与信息工程学院,福州350108

出　　处：《中国生物医学工程学报》2022年第1期48-56,共9页Chinese Journal of Biomedical Engineering

基　　金：福建省自然科学基金(2020J01472)。

摘　　要：结肠镜检查广泛应用于结直肠癌的早期筛查和诊疗,但仅靠人工判读结肠息肉漏检率较高,有研究统计可达25%。基于深度学习的计算机辅助技术有助于提高息肉检测率,但目前深度学习的主流分割网络U-Net存在着两个局限:一是编解码的输出特征图之间存在着语义鸿沟;二是U-Net的双层卷积单元无法学习多尺度信息;割裂地看待容易使模型陷入局部最优。提出一种基于同构化改进的U-Net网络,不仅能缓解编解码特征间的语义鸿沟,且具备提取多尺度特征的能力。首先,在U-Net编解码器和跳层路径中,引入同构单元IU构成同构网络I-Net,以减少编解码器之间的语义鸿沟;接着,结合密集连接和残差连接的优点,设计密集残差单元DRU以学习多尺度信息;最后,将同构网络的处理单元初始化为密集单元,构成基于密集残差单元的同构网络DRI-Net。使用包含612幅结直肠镜息肉图像的公开数据集CVC-ClinicDB,采用5折交叉验证评估所提出的模型,DRI-Net可得Dice系数为90.06%,交并比(IoU)为85.52%,与U-Net相比,Dice系数提升8.50%,IoU提升11.03%。此外,在国际ISIC2017皮肤镜挑战数据集上验证模型在其他模态数据的泛化性,2000幅训练,600幅测试,获得的Dice系数为86.57%,IoU为79.20%,与ISIC 2017排行榜第一名的方法相比,Dice系数提升1.67%,IoU提升2.70%。实验表明,DRI-Net能有效解决U-Net存在的局限,且泛化性良好。Colonoscopy is a widely used technique for colon screening and polyp lesions diagnosis.Nevertheless,manual screening using colonoscopy suffers from a miss rate around 25%of polyps.Deep learning-based computer-aided diagnosis(CAD)for polyp detection has potentials of reducing the human errors.Polyp detection depends on encoder-decoder network(U-Net)for polyp segmentation.However,U-Net has two limitations,one is that the semantic gap exists between the feature maps from the encoder and decoder;the other one is convolutional layers in the encoder-decoder processing units fail to extract multi-scale information.In this work,we proposed an identical network(I-Net)to tackle the problems in a consolidated manner.The I-Net introduced identical units(IU)both in skip connections and encoder-decoder sub-networks of U-Net to reduce the semantic gap.Meanwhile,motivated by the dense and residual connections,we designed a dense residual unit(DRU)to learn multi-scale information.Finally,DRI-Net was developed by initializing IU to DRU,which not only alleviated the semantic gap between the encoder and the decoder but also learned multi-scale features.We evaluated the proposed methods on the CVC-ClinicDB dataset containing 612 colonoscopy images through five-fold cross validation.Experimental results demonstrated that the DRI-Net achieved Dice coefficient of 90.06%and intersection over union(IoU)of 85.52%.Compared to the U-Net,DRI-Net improved the Dice coefficient of 8.50%and IoU of 11.03%.In addition,we studied the generalization of the proposed methods on International Skin Imaging Collaboration(ISIC)2017 dataset including a training set of 2000 dermoscopy images for model training and a test set of 600 images for model evaluation.The study indicated that the I-Net achieved Dice coefficient of 86.57%and IoU of 79.20%.Compared to the first-place solution on ISIC 2017 leaderboard,the DRI-Net improved Dice coefficient of 1.67%and IoU of 2.70%.In conclusion,the results demonstrated that DRI-Net effectively overcome the limitations of

关 键 词：息肉分割 深度学习 同构网络 

分 类 号：R318[医药卫生—生物医学工程]