TABLE II. Segmentation Accuracy for our RBC Polygon Set, Using 11-fold Cross Validation. For Each Experiment, the Training Set Contains Tiles From 150 Images, and the Test Set Contains 15 Images. We Conducted t-Tests Using the F1-Measure Between our Proposed RBCNet Dual Network (U-Net+Faster RCNN) and Other Methods. All p-Values are < 0.001, Indicating That the Differences Between Groups are Statistically Significant.
Method  Evaluation Metrics |
F1-Measure 
|
Precision 
|
Recall 
|
|---|---|---|---|
| Traditional methods | |||
| Watershed [17] |  |
 |
 |
| Active contour [18], [19] |  |
 |
 |
| Instance segmentation deep learning methods | |||
| SegNet [48] |  |
 |
 |
| U-Net [42] |  |
 |
 |
| DeepLab v3+ [49] |  |
 |
 |
| Object detection deep learning methods | |||
| Faster R-CNN [39] on overlapping-tiles + NMS |  |
 |
 |
| Yolo [50] on overlapping-tiles + NMS |  |
 |
 |
| SSD [51] on overlapping-tiles + NMS |  |
 |
 |
| Mask R-CNN [52] on overlapping-tiles + NMS |  |
 |
 |
| Proposed dual deep learning networks | |||
| SegNet + Faster R-CNN |  |
 |
 |
| U-Net + YOLO |  |
 |
 |
| RBCNet (U-Net + Faster R-CNN) |  |
 |
 |