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TigerBx: Tissue Mask Generation for Brain Extration
Validation pipeline (after v0.1.16)
import tigerbx
import time
t = time.time()
# tigerbx.val(argstring, input_dir, output_dir=None, model=None, GPU=False, debug=False)
df, metric1 = tigerbx.val('bet_NFBS', 'NFBS_Dataset','temp', GPU=True)
df, metric2 = tigerbx.val('bet_synstrip', 'synthstrip_data_v1.4','temp', GPU=True)
df, metric3 = tigerbx.val('aseg_123', 'aseg', 'temp', GPU=True)
df, metric4 = tigerbx.val('dgm_123', 'aseg', 'temp', GPU=True)
df, metric5 = tigerbx.val('syn_123', 'aseg', 'temp', GPU=True)
df, metric6 = tigerbx.val('reg_60', 'aseg', 'temp', GPU=True, template='Template_T1_tbet.nii.gz')
print('bet_NFBS', metric1)
print('bet_synstrip', metric2)
print('aseg_123', metric3)
print('dgm_123', metric4)
print('syn_123', metric5)
print('reg_60', metric6)
print('Time', time.time() - t)
Validation Datasets
- Skull Stripping: NFBS_Dataset
- Skull Stripping: Synstrip
- Deep gray matter: MindBoggle-101
- Deep gray matter: CANDI
- Registration: ABIDE
Internal Validation Sets:
- aseg: A combination of MindBoggle-101 and CANDI (N = 123) with DGM labels
Validation Results of Verion 0.1.16
Deep Gray matter: Dice coefficients
| Structure | L/R | aseg | dgm | syn | L/R | aseg | dgm | syn |
|------------|-----|-------|------|------|-----|-------|------|------|
| Thalamus | L | 0.879 | 0.898| 0.890| R | 0.889 | 0.902| 0.884|
| Caudate | L | 0.875 | 0.874| 0.850| R | 0.875 | 0.872| 0.845|
| Putamen | L | 0.873 | 0.885| 0.847| R | 0.862 | 0.880| 0.829|
| Pallidum | L | 0.827 | 0.827| 0.828| R | 0.814 | 0.815| 0.794|
| Hippocampus| L | 0.808 | 0.828| 0.789| R | 0.810 | 0.831| 0.779|
| Amygdala | L | 0.737 | 0.764| 0.716| R | 0.727 | 0.750| 0.711|
| Mean | L | 0.833 | 0.846| 0.820| R | 0.829 | 0.841| 0.807|
Registration:
mean dice: 0.797
mean dice: 0.804(FuseMorph)
Skull Stripping
bet_NFBS: 0.973
bet_synstrip: 0.971