Y-Net Automatic brain tumor segmentation software based on deep learning method
Introduction
Our software contains an end-to-end CNN segmentation network, which uses only two adjacent images instead of the target image as the input data of the deep neural network to predict the brain tumor area in the target image. This method avoids noise interference in the target image and makes full use of the spatial context features between adjacent slices to obtain accurate brain tumor segmentation results.
Publications
Credits
- Fei Guo fguo@tju.edu.cn Investigator
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, China
- Shiqiang Ma shiqiang@tju.edu.cn Developer
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, China
- Xuejian Li lixuejian@tju.edu.cn Developer
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, China
Community Ratings
Usability | Efficiency | Reliability | Rated By |
---|---|---|---|
0 user | |||
Sign in to rate |
Summary
Accession | BT007128 |
---|---|
Tool Type | Application |
Category | Image segmentation |
Platforms | Linux/Unix |
Technologies | Python3 |
User Interface | Terminal Command Line |
Latest Release | 1.0 (May 29, 2021) |
Download Count | 296 |
Country/Region | China |
Submitted By | Fei Guo |
Fundings
2018YFC0910400