# Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans
## 3 Results  
As for the strong similarity between adjacent slices in CT scans, we assume that the contribution of some slices could be replaced by others in the training process. To test this idea, we select partial cases at a certain ratio from the whole training dataset, based on their simple statistical information (e.g., the number of slices in volume, proportion of positive samples and negative samples in volume). And then the selected cases were enhanced by flipping and mirroring. As shown in Table 1, reducing the number of training samples within a certain range has less influence on the segmentation ability of FCN. However, the prediction ability of FCN decreases significantly when the selection of training samples is further reduced. The max value, which refers to the best segmentation results of the model, has little change in different selection ratio (the ratio of training scans in part B to total number of scans in training dataset) experiments. Meanwhile, the min value, referring to the worst segmentation results of the model, decreases significantly when training samples decrease substantially. These phenomena are also observed in kidney segmentation and spleen segmentation from CT scans using FCN model, as shown in Tables A1 and A2. Moreover, the same results were also discovered in liver segmentation, kidney segmentation and spleen segmentation tasks using U-Net model, as shown in Tables A3-A5. These results suggest that there is redundancy in the training dataset, and that too little training data is harmful in the model training process. As for the performance of FCN model begins to decline significantly when the selection ratio is less than 0.5, so we set selection ratio as 0.5 in the proposed model, and divide the training dataset into two parts (A and B) in liver segmentation. Slices in part B are used for model training, and we get the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dice Score**} \\ \cline{2-4}  & **Mean** & **Min** & **Max** \\ \hline selection ratio = 1, baseline & 0.9705 \(\pm\) 0.011 & 0.923 & 0.9856 \\ selection ratio = 0.8 & 0.9706 \(\pm\) 0.011 & 0.921 & 0.9853 \\ selection ratio = 0.5 & 0.9702 \(\pm\) 0.013 & 0.9124 & 0.9843 \\ selection ratio = 0.3 & 0.9512 \(\pm\) 0.035 & 0.8635 & 0.9844 \\ selection ratio = 0.2 & 0.9475 \(\pm\) 0.039 & 0.812 & 0.9817 \\ proposed model & 0.9789 \(\pm\) 0.012 & 0.947 & 0.9854 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Liver segmentation results on test dataset based on different selection ratio using FCN model. pre-training model after \(5\times 10^{5}\) iterations. Using the pre-training model to predict slices in part A, we then simply classify these slices in part A into two categories, i.e., hard and easy simples, based on their Dice score. In the liver segmentation task using FCN model, we set the threshold to 0.923, the min Dice scores of baseline. Six thousand, two hundred and sixty-eight slices are classified as hard samples; however, 35,984 slices are classified as easy samples, almost 6-fold the number of hard samples. Hard samples in part A were enhanced by flipping and added to the dataset (part B) used in pre-training process. Then, we continue the training process until model reaching \(10\times 10^{5}\) iterations. As shown in Table 1, the proposed model performs slightly better than the baseline in liver segmentation with a smaller training dataset. Moreover, adding hard examples has almost no effect on the max value of Dice score, but it can significantly increase the min value compared with the baseline. This indicates that increasing the ratio of hard samples in the training dataset has little influence on easily segmented cases, but could greatly improve the segmentation ability of model on hard samples. The segmentation results display in 3D form in Figure 3A,B show that the proposed method could enhance liver segmentation results, especially in some details.