# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 4 Experimental Evaluation
### Experimental Set-Up  
All experiments were conducted under the same conditions. Considering that CNN-based methods need a large number of image data and that the number of training data were minimal for both datasets, to avoid overfitting, data augmentation methods were used to expand the training data. To this aim, we used random cropping, vertical and horizontal flipping (already implemented in the MATLAB programming language and available at [[https://github.com/appiek/Nuclei_Segmentation_Experiments_Demo](https://github.com/appiek/Nuclei_Segmentation_Experiments_Demo)]([https://github.com/appiek/Nuclei_Segmentation_Experiments_Demo](https://github.com/appiek/Nuclei_Segmentation_Experiments_Demo))). The random cropping procedure extracted 20 random crops of size 224 that were flipped vertically and horizontally with a probability of 50%. This procedure increased the original training sets by twenty times, so the total numbers of training images for MIC17 and BNS were 480 and  
Figure 2: Example of the images present in the used datasets: on top, the images belonging to MIC17 (GBM, LGG, HNSC and LUSC, respectively), and on bottom, the images belonging to BNS. 460, respectively. The training processes were carried out for 300 epochs with a learning rate of 0.0002, decreased by a factor of 0.8 per epoch. For the DIMAN loss function, we used the parameters suggested by the same authors [23]. For fairness, we did not exploited the post-processing procedures provided in the respective papers, since they could have significantly influenced the result by benefitting specific methods and not benefitting others. We conducted all the experiments on a single machine with the following hardware configuration: Intel(R) Core(TM) i9-8950 HK @ 2.90 GHz CPU with 32 GB RAM and NVIDIA GTX1050 Ti 4 GB GPU. To evaluate the performances on nuclei segmentation, we used the same evaluation metrics provided by the gland segmentation challenge contest (already implemented in the MATLAB programming language and available at [[https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/evaluation/](https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/evaluation/)]([https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/evaluation/](https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/evaluation/))); they were F1-score (F1), precision (P), recall (R), object-level Dice index (ODI) and object-level Hausdorff distance (OHD). The first three metrics were used to evaluate the counting and object-level nuclei segmentation performances. \(F1\) is the average harmonic mean between the \(P\) and \(R\) for each object, where \(P\) and \(R\) gives information on the number of correctly segmented cells; in particular, \(P\) penalises oversegmentation (the lower the \(P\) value, the higher the oversegmentation) and \(R\) penalises undersegmentation (the lower the \(R\) value, the higher the undersegmentation). \(F1\), \(P\) and \(R\) values range over the interval \([0,1]\) and are computed following Equations (1)-(3):  
\[F=\frac{2 RP}{R+P} \tag{1}\]  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
where \(TP\) (true positive) indicates the number of segmented cells that are present in the ground truth image, \(FP\) (false positive) provides the number of segmented cells that are not present in the ground truth image and \(FN\) (false negative) gives the number of ground truth cells that are not present in the segmented image. The last two metrics instead were used to evaluate not only the object-level but also the pixel-level nuclei segmentation performances. The usual Dice index measures the similarity between the set of pixels belonging to the ground truth image \(G\) and the set of pixels belonging to the segmented image \(S\) as follows:  
\[\text{Dice}(G,S)=\frac{2|G\cap S|}{|G|+|S|} \tag{4}\]  
Instead, the ODI also takes into account the information of individual cells.