中圖分類號： TN911.73；TP751.1 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.200923 中文引用格式： 劉志華，王正業，李豐軍，等. Faster RCNN和LGDF結合的肝包蟲病CT圖像病灶分割[J].電子技術應用，2021，47(7)：33-37，43. 英文引用格式： Liu Zhihua，Wang Zhengye，Li Fengjun，et al. CT image segmentation of liver hydatid disease based on Faster RCNN and LGDF[J]. Application of Electronic Technique，2021，47(7)：33-37，43.
CT image segmentation of liver hydatid disease based on Faster RCNN and LGDF
Liu Zhihua1，Wang Zhengye1，Li Fengjun2，Yan Chuanbo2
1.College of Public Health，Xinjiang Medical University，Urumqi 830011，China； 2.College of Medical Engineering Technology，Xinjiang Medical University，Urumqi 830011，China
Abstract： 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.
Key words : faster RCNN；LGDF；deep learning；object detection；lesion segmentation