Faster RCNN和LGDF结合的肝包虫病CT图像病灶分割  

CT image segmentation of liver hydatid disease based on Faster RCNN and LGDF

作　　者：刘志华 王正业 李丰军[2] 严传波[2] Liu Zhihua;Wang Zhengye;Li Fengjun;Yan Chuanbo(College of Public Health,Xinjiang Medical University,Urumqi 830011,China;College of Medical Engineering Technology,Xinjiang Medical University,Urumqi 830011,China)

机构地区：[1]新疆医科大学公共卫生学院,新疆乌鲁木齐830011 [2]新疆医科大学医学工程技术学院,新疆乌鲁木齐830011

出　　处：《电子技术应用》2021年第7期33-37,43,共6页Application of Electronic Technique

基　　金：国家自然科学基金(81560294)。

摘　　要：针对人工阅片工作量大、阅片质量不佳且容易出现漏检、错判等问题,将Faster RCNN目标检测模型应用于肝包虫病CT图像的检测,并对目标检测模型进行改进:基于图片分辨率低、病灶大小不同的特点,使用网络深度更深的残差网络(ResNet101)代替原来的VGG16网络,用以提取更丰富的图像特征;根据目标检测模型得出的病灶坐标信息引入LGDF模型进一步对病灶进行分割,从而辅助医生更高效的诊断疾病。实验结果表明,基于ResNet101特征提取网络的目标检测模型能够有效提取目标的特征,检测准确率相比原始检测模型提高2.1%,具有较好的检测精度。同时,将病灶坐标信息引入LGDF模型,相比于原始的LGDF模型更好地完成了对肝包虫病病灶的分割,Dice系数提高了5%,尤其对多囊型肝包虫病CT图像的分割效果较好。In view of the large workload of manual image reading,poor image reading quality,and prone to missed inspections and wrong judgments,in this paper,the faster RCNN target detection model is applied to the detection of hepatic echinococcosis CT images.And the target detection model is improved:based on the characteristics of low image resolution and different lesion sizes,the residual network with deeper network depth(ResNet101)is used to replace the original VGG16 to extract richer image features;according to the coordinate information of the lesion obtained by the object detection model,the LGDF model is introduced to further segment the lesion to assist doctors in diagnosing the disease more efficiently.The experimental results show that the object detection model based on the ResNet101 feature extraction network can effectively extract the features of the target,and the detection accuracy is 2.1%higher than the original detection model,and it has better detection accuracy.At the same time,the coordinate information of the lesion is introduced into the LGDF model.Compared with the original LGDF model,the segmentation of hepatic hydatid lesions is better completed,the Dice coefficient is increased by 5%,and the segmentation effect is better especially for the multi cystic liver hydatidosis CT image.

关 键 词：faster RCNN LGDF 深度学习 目标检测 病灶分割 

分 类 号：TN911.73[电子电信—通信与信息系统] TP751.1[电子电信—信息与通信工程]