Table 1 Model performance/reliability measured by SDC/HD with/without curation, per cost function and for set window versus patient-specific window
From: Strategies to improve deep learning-based salivary gland segmentation
Set sizes (SMG/PG) | SDC | HD | ||||||
|---|---|---|---|---|---|---|---|---|
Train | Validation | Test | SMG | PG | SMG | PG | ||
Data quality | ||||||||
Without augmentation | ||||||||
Train data | Test data | |||||||
Clinical | Clinical | 18/18 | 2/2 | 4/4 | .68 ± .06 | .68 ± .05 | 17.6 ± 1.5 | 24.7 ± 5.1 |
Curated | Curated | 18/18 | 2/2 | 4/4 | .66 ± .07 | .68 ± .04 | 23.4 ± 1.3 | 28.1 ± 4.3 |
With augmentation | ||||||||
Train data | Test data | |||||||
Clinical | Clinical | 90/90 | 2/2 | 4/4 | .67 ± .06 | .69 ± .06 | 13.6 ± 1.3 | 24.8 ± 5.3 |
Curated | Curated | 90/90 | 2/2 | 4/4 | .67 ± .07 | .69 ± .04 | 12.0 ± 1.5 | 21.8 ± 4.6 |
Cost functions | ||||||||
SDC | 90/90 | 10/10 | 20/20 | .71 ± .06 | .71 ± .06 | 6.9 ± 1.5 | 17.3 ± 6.4 | |
SDC(0.5) | 90/90 | 10/10 | 20/20 | .71 ± .06 | .71 ± .06 | 9.0 ± 3.0 | 17.4 ± 6.8 | |
SDC(0.05) | 90/90 | 10/10 | 20/20 | .70 ± .06 | .71 ± .06 | 6.6 ± 1.6 | 16.6 ± 7.0 | |
SDC + HD | 90/90 | 10/10 | 20/20 | .70 ± .05 | 7.6 ± 2.4 | |||
Patient-specific windowing | ||||||||
Set window | 940/1024 | 94/114 | 188/227 | .86 ± .07 | .85 ± .05 | 4.5 ± 1.9 | 8.1 ± 3.8 | |
Patient-specific window | 940/1024 | 94/114 | 188/227 | .87 ± .05 | .87 ± .04 | 4.1 ± 1.6 | 7.7 ± 3.8 | |