Name
Title | ||
|---|---|---|
An Unsupervised Learning-Based Multi-Organ Registration Method For 3d Abdominal Ct Images |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21186254 | SENSORS |
Keywords | DocType | Volume |
registration, convolutional neural network, medical image, abdominal CT | Journal | 21 |
Issue | ISSN | Citations |
18 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaodi Yang | 1 | 0 | 0.34 |
| Yu-Qian Zhao | 2 | 92 | 9.98 |
| Miao Liao | 3 | 23 | 3.20 |
| Fan Zhang | 4 | 229 | 69.82 |