基于改进U-Net的下肢骨骼CT图像分割  

CT Image Segmentation of Lower Limb Bones Based on Improved U-Net

作　　者：刘金辉[1] 童晶[1] 倪佳佳 黎学飞 张旭[1] LIU Jin-Hui;TONG Jing;NI Jia-Jia;LI Xue-Fei;ZHANG Xu(College of Internet of Things Engineering,Information Department,Hohai University,Changzhou 213022,China)

机构地区：[1]河海大学信息学部物联网工程学院,常州213022

出　　处：《计算机系统应用》2022年第10期134-141,共8页Computer Systems & Applications

基　　金：国家自然科学基金(61772172)

摘　　要：针对骨骼CT图像对比度较低、特征不明显、现有算法对骨骼特征提取不充分的问题,本文提出了一种基于U-Net的改进网络来实现骨骼数据的精确分割.在网络编码阶段,使用密集连接的空洞卷积模块加强骨骼特征的提取;在网络解码阶段,使用结合注意力机制的融合模块充分利用空间信息与语义信息,改善骨骼信息丢失的问题.改进算法在人体下肢骨骼CT数据集中Dice系数达89.44%,IoU系数达80.55%.与U-Net模型相比,Dice系数提高了5.1%,IoU系数提高了7.63%.实验结果表明,提出的优化算法对下肢骨骼CT图像可以达到精确分割的效果,对骨科疾病的治疗与术前规划提供了参考.This study proposes an improved U-Net for precise segmentation of bone data to solve the problems of low contrast,indistinct features,and insufficient extraction of bone features by existing algorithms in bone computed tomography(CT)images.In the network coding stage,the densely connected dilated convolution module is used to enhance the extraction of bone features;in the network decoding stage,the attention-based fusion module is adopted to make full use of spatial information and semantic information and thereby avoid the loss of bone information.When the improved algorithm is applied to a CT dataset of human lower limb bones,the Dice coefficient is 89.44%,and the intersection over union(IoU)coefficient is 80.55%.Compared with those obtained with the U-Net model,the Dice coefficient is increased by 5.1%,and the IoU coefficient is improved by 7.63%.The experimental results show that the proposed optimization algorithm can be employed to achieve precise segmentation of CT images of lower limb bones.It also provides a reference for the preoperative planning for orthopedic diseases and subsequent treatment.

关 键 词：U-Net 骨骼分割 空间注意力 通道注意力 特征融合 语义分割 注意力机制 

分 类 号：R816.8[医药卫生—放射医学] TP391.41[医药卫生—临床医学]