Table 6 Segmentation performance for the ATUM-SEM dataset
From: Effective automated pipeline for 3D reconstruction of synapses based on deep learning
Size of pixel | Metrics | Sun [18] | U-Net [23] | F-RCNN+ | F-RCNN + | F-RCNN + fusion + |
|---|---|---|---|---|---|---|
overlap | Â | Â | Â | Growing [19] | GrabCut | continuity + GrabCut |
0-pixel | AP | 32.7% | 55.4% | 49.0% | 57.6% | 65.6% |
| Â | F1 | 36.3% | 58.1% | 53.4% | 60.6% | 65.2% |
| Â | Jaccard | 30.6% | 49.5% | 43.2% | 52.6% | 58.1% |
3-pixel | AP | 56.3% | 67.4% | 67.8% | 78.8% | 86.1% |
| Â | F1 | 60.6% | 66.9% | 69.2% | 75.8% | 81.5% |
| Â | Jaccard | 50.1% | 59.0% | 59.8% | 69.0% | 74.7% |
5-pixel | AP | 59.4% | 70.6% | 76.4% | 80.3% | 88.6% |
| Â | F1 | 62.7% | 70.1% | 74.4% | 77.5% | 84.1% |
| Â | Jaccard | 53.7% | 60.2% | 63.5% | 72.2% | 76.9% |