Segmentation of brain tumor on magnetic resonance images using 3D full-convolutional densely connected convolutional networks.
- Author:
Yi-Hui HUANG
1
;
Qian-Jin FENG
Author Information
1. 1School of Biomedical Engineering, 2Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China. E-mail: hyh261@163.com.
- Publication Type:Journal Article
- From:
Journal of Southern Medical University
2018;38(6):661-668
- CountryChina
- Language:Chinese
-
Abstract:
Accurate segmentation of multiple gliomas from multimodal MRI is a prerequisite for many precision medical procedures. To effectively use the characteristics of glioma MRI and im-prove the segmentation accuracy, we proposes a multi-Dice loss function structure and used pre-experiments to select the good hyperparameters (i.e. data dimension, image fusion step, and the implementation of loss function) to construct a 3D full convolution DenseNet-based image feature learning network. This study included 274 segmented training sets of glioma MRI and 110 test sets without segmentation. After grayscale normalization of the image, the 3D image block was extracted as a network input, and the network output used the image block fusion method to obtain the final segmentation result. The proposed structure improved the accuracy of glioma segmentation compared to a general structure. In the on-line assessment of the open BraTS2015 data set, the Dice values for the entire tumor area, tumor core area, and enhanced tumor area were 0.85, 0.71, and 0.63, respectively.
