Research Article
Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN
Table 4
Comparison of deep learning approaches for brain structure segmentation.
| Authors | CNN style | Dimension | Accuracy | Data |
| Zhang et al. [13] | Patchwise | 2D | DSC 83.5% (CSF), 85.2% (GM), 86.4% (WM) | Private data (10 healthy infants) | Nie et al. [14] | Semantic-pixelwise | 2D | DSC 85.5% (CSF), 87.3% (GM), 88.7% (WM) | Private data (10 healthy infants) | de Brebisson et al. [15] | Patchwise | 2D/3D | Overall DSC 72.5% ∓ 16.3% | MICCAI 2012-multi-atlas labeling | Moeskops et al. [16] | Patchwise | 2D/3D | Overall DSC 73.53% | MICCAI 2012-multi-atlas labeling | Proposed method | Pixel-label semantic (SegNet CNN) | 2D | DSC 72.2% (CSF), 74.6% (GM), 81.9% (WM) | OASIS cross-sectional MRI |
|
|