Table 1.
Features and hyper-parameters used to train the random forest model.
| Classes | Features or filters | Hyper-parameters | Modality |
|---|---|---|---|
| Size | Volume | – | T1w |
| Maximum 3D distance | – | ||
| Major and minor axis ratio | – | ||
| Texture | Max | – | T1w & FLAIR |
| Min | – | ||
| Median | – | ||
| Mean | – | ||
| Variance | – | ||
| Energy | – | ||
| Standard deviation | – | ||
| Skewness | – | ||
| Kurtosis | – | ||
| Root mean square | – | ||
| Range | – | ||
| Inter quartile range | 0.25–0.75 | ||
| Entropy | – | ||
| Uniformity | – | ||
| Percentile | 2.5, 25, 50, 75, 97.5 | ||
| Multi-scale deep | 2D Average | Kernel size = 3 | T1w & FLAIR |
| 2D Disk | Radius = 1 | ||
| 2D Gaussian | Kernel size = 3 σ = 0.5, 1, 1.5, 2 |
||
| 2D Log of Gaussian | Kernel size = 3 σ = 0.5, 1, 1.5, 2 |
||
| 2D Laplacian | σ = 0, 0.25, 0.5, 0.75, 1 | ||
| 2D Prewitt | Direction = 0, 90, 180, 270° | ||
| 2D Sobel | Direction = 0, 90, 180, 270° | ||
| 2D Motion | Length = 3, angle = 25, 50° | ||
| 3D Average | Kernel size = 3 | ||
| 3D Ellipsoid | Kernel size = 3 | ||
| 3D Gaussian | Kernel size = 3 σ = 0.5, 1, 1.5, 2 |
||
| 3D Log of Gaussian | Kernel size = 3 σ = 0.5, 1, 1.5, 2 |