Table 3.
Research on brain lesion segmentation.
| Authors (Year) | Method | Medical Image | Performance | Notes |
|---|---|---|---|---|
| Brain tumor | ||||
| Havaei et al. [62] (2016) | Deep CNN | Magnetic resonance images | DC 1: 0.88 | Cascade architecture using pre-output concatenation |
| Pereira et al. [63] (2016) | CNN-based | Magnetic resonance images | DC: 0.88 | Patch extraction from an image before entering the CNN |
| Isensee et al. [16] (2018) | 3D U-Net | Magnetic resonance images | DC: 0.85 | Modified from U-Net; summation for multi-level features |
| Xu et al. [33] (2020) | U-Net | Magnetic resonance images | DC: 0.87 | Attention-U-Net |
| McKinley et al. [64] (2018) | deepSCAN | Magnetic resonance images | Mean DC ET 2: 0.7 WT 3: 0.86 TC 4: 0.71 |
Bottleneck CNN design; dilated convolution |
| Stroke | ||||
| Wang et al.[65] (2016) | Deep Lesion Symmetry ConvNet | Magnetic resonance images | Mean DSC 5: 0.63 | Combined unilateral (local) and bilateral (global) voxel descriptor |
| Monteiro et al. [66] (2020) | DeepMedic | Computed tomography | Differs according to size | Three parallel 3D CNNs for different resolutions |
| Zhang et al. [61] (2020) | U-Net | Magnetic resonance images | DSC: 0.62 IoU 6: 0.45 |
FPN for extraction first |
1 Dice coefficient, 2 enhanced tumor, 3 whole tumor, 4 tumor core, 5 Dice similarity coefficient, 6 intersection over union.