Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network.
10.7507/1001-5515.201902006
- Author:
Zhiyong HUO
1
;
Shuaiyu DU
2
;
Zhao CHEN
2
;
Weida DAI
2
Author Information
1. Jiangsu Province Key Lab on Image Processing & Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China.huozy@njupt.edu.cn.
2. College of Telecommunication & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain glioma segmentation;
densely connected block;
magnetic resonance imaging;
three-dimensional convolutional neural network
- MeSH:
Brain Neoplasms;
diagnostic imaging;
Glioma;
diagnostic imaging;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging;
Neural Networks (Computer)
- From:
Journal of Biomedical Engineering
2019;36(5):763-768
- CountryChina
- Language:Chinese
-
Abstract:
Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.
