An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images

Sensors

Yuqian Zhao¹  Fan Zhang¹  Shaodi Yang¹  Miao Liao² 

[1] School of Automation, Central South University, Changsha 410083, China;School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China;

关键词: registration;    convolutional neural network;    medical image;    abdominal CT;   

DOI  :  10.3390/s21186254

来源: DOAJ

【 摘 要 】

Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.

【 授权许可】

Unknown