Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network

Table 2

A summary of brain tumor segmentation methods based on deep-learning neural networks. Only methods using MRI data were included in this table.

Number	Publication	Database	Summary of method	Performance (DICE)
				Complete	Core	Enh
1	Urban et al. [14]	MICCAI-BRATS 2013 dataset	3D CNN with 3D convolutional kernels	0.87	0.77	0.73
2	Zikic et al. [15]	MICCAI-BRATS 2013 dataset	Apply a CNN in a sliding-window fashion in the 3D space	0.84	0.74	0.69
3	Davy et al. [16]	MICCAI-BRATS 2013 dataset	A CNN with two pathways of both local and global information	0.85	0.74	0.68
4	Dvorak and Menze [17]	MICCAI-BRATS 2013 dataset	Structured prediction was used together with a CNN	0.83	0.75	0.77
5	Pereira et al. [18]	MICCAI-BRATS 2013 dataset	A CNN with small 3 × 3 kernels	0.88	0.83	0.77
6	Havaei et al. [19]	MICCAI-BRATS 2013 dataset	A cascade neural network architecture in which “the output of a basic CNN is treated as an additional source of information for a subsequent CNN”	0.88	0.79	0.73
7	Lyksborg et al. [20]	MICCAI-BRATS 2014 dataset	An ensemble of 2D convolutional neural networks +doing a volumetric segmentation by three steps	0.80	0.64	0.59
8	Kamnitsas et al. [21]	MICCAI-BRATS 2015 dataset	Using 3D CNN, two-scale extracted feature, 3D dense CRF as postprocessing	0.85	0.67	0.63