Table 1.
Previous work on location of glomeruli and the performance obtained.
| Source | Data/slide | Approach | Performance |
|---|---|---|---|
| Temerinac‐Ott et al [14] |
Train: 16 Test: 4 |
Mutual information + CNN | 68.94% F1 |
| Kato et al [15] |
Train: * Test: 20 |
S‐HOG + SVM | 86.6% F1 |
| Marée et al [16] | Total: 200 | Ellipsoidal shape + decision tree | 87% F1 |
| Gadermayr et al [17] | Total: 24 | U‐Net | 90% Dice |
| Gallego et al [18] | Total: 40 | AlexNet | 93.7% F1 (pixel‐wise) |
| Sheehan et al [19] | Total: 90 | AlexNet + SVM | 92% recall, 90% specificity |
| Bukowy et al [20] |
Train: 72 Test: 13 |
Faster RCNN | 96.9% precision, 96.8% recall |
| Bueno et al [21] |
Train: 38 Test: 9 |
SegNet‐VGG19 |
99.8% precision, 99.2% F1 (pixel‐wise) |
| Sarder et al [22] |
Train: * Test: 15 |
Gabor filters | 87.8% accuracy |
| Ginley et al [23] |
Train: 41 Test: 13 |
DeepLab v2 | 93% accuracy |
| Simon et al [24] |
Train: 25 Test: 9 |
Multi‐radial LBP + SVM | 90.4% precision, 76.7% recall |
| Hermsen et al [25] |
Train: 37 Test: 3 |
5 U‐Nets ensemble | 79% Dice |
| Comparison I |
Train: 360 Test: 40 |
U‐Net | 89.2% precision, 90.6% recall |
| Comparison II | U‐Net‐SSIM | 91.4% precision, 92.1% recall | |
| Comparison III | Mask R‐CNN [26] | 88.1% precision, 92.3% recall | |
| Comparison IV | FCOS [27] | 91.7% precision, 92.8% recall | |
| APRS | U‐Net‐SSIM + marked watershed |
93.1% precision, 94.0% F1 94.9% recall, 90.1% Dice |
*
Not mentioned. Dice, dice coefficient; F1, F1 score.