## 3 Results
Thus, the training process starts considering those initial weights. In our case, the ImageNet weights were the initial weights. The ImageNet Challenge [51] differentiates 1000 usual objects, but has nothing to do with medical image segmentation. Due to applying the transfer learning technique, we were able to obtain better results with a smaller number of epochs than training the system without this technique. Figure 8 depicts the training loss over the progress of epochs for the six CNNs  
Figure 7: Encoder-Decoder Architecture: 2 variants of encoder architectures and 3 types of decoder architecture, in total 6 CNN architectures. presented. It is obvious that the larger encoder architectures using ResNet101 are steeper and converge slightly faster in the training process. Figure 9 and Table 1 show the overall validation accuracy for the networks presented. From the perspective of the validation accuracy for all classes, the PSP-ResNet101 is the best, followed by FCN-ResNet101 and DeepLab-ResNet101. The overall validation accuracy is measured on the validation set and is obtained as the mean accuracy (Equation (2)) over all four classes. \[Accuracy=\frac{Correct\ pixels\ inpredicted\ segmentation}{Total\ pixels\ in\ segmentation}\, \tag{1}\]  
\[meanAccuracy=\frac{1}{|Classes|}\sum_{c\in Classes}\frac{Correct\ pixels\ in\ predicted\ segmentation\ of\ class\ c}{Total\ pixels\ in\ segmentation\ of\ class\ c}\, \tag{2}\]  
It can be observed that the DeepLab-ResNet50 was stopped at epoch 49 because it had the highest training loss and lowest validation accuracy from all the six architectures.The best architecture from the perspective of overall accuracy are the DeepLab-ResNet101, PSP-ResNet101, FCN-ResNet101, and FCN-ResNet50. The stopping condition of the final epoch was set by considering the stopping patience of four epochs with non-decreasing training loss, and the stopping tolerance was set to 0.001. This was the reason for different stopping epochs of the networks (Table 1). Figure 8: Training Loss: the best losses are obtained by the 3 architectures based on the ResNet101 encoder. Figure 9: Validation Accuracy: the best architectures based on the validation accuracy are: PSP-ResNet101 (accuracy of 0.9604), DeepLab-ResNet101 (accuracy of 0.9479), FCN-ResNet101 (accuracy of 0.9576), and FCN-ResNet50 (accuracy of 0.9516). The quantitative evaluation of different architectures was conducted by computing the Dice score on the test set. The Sorensen-Dice score measures the similarity of two samples; in our case, the similarity between the ground truth (GT) and the segmentations obtained (predicted segmentations). It is twice the overlap area over the cardinality of both sets (Equation (3)). In the case of binary segmentation, it can be expressed by the 2 \(\times\) TPR (true positive rate) over the total number of pixels: 2 \(\times\) TPR+FPR (false positive rate)+FNR (false negative rate). \[DiceScore=\frac{2\times|Pred\cap GT|}{|Pred|+|GT|}=\frac{2\times TPR}{2\times TPR +FPR+FNR}\enspace, \tag{3}\]  
In the case of multi-class classification, the Dice score is computed considering class \(i\) and class \(non-i\) for all other pixels. In the training process of the six different networks, we used the mIOU (Equation (9)) as the loss function of the optimization algorithm. The mean intersection over reunion is a region-based loss function, also called the Jaccard loss. The Jaccard loss and the Dice loss [52] are very similar losses, and they can be alternatively used in segmentation tasks. Figure 5:0  
Figure 5:1  
The Tversky Distance [53] is a generalization of the Dice loss that considers the true positive pixels over a weighted sum of true positives and false positives and false negatives. Figure 5:2  
We have considered this type of loss too, but it can improve the training loss if it is considered on binary segmentation cases. In our case, the coefficients weighting the FP and FN in the nominator have to be setup separately form one class to the other.