# Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans
## 4 Discussion  
It is often thought that the more data, the better the performance in deep learning. However, in this work, we observed that a proper reduction of training samples in training process had little effect on the segmentation performance of model. This may be due to the strong similarity between two adjacent slices in CT images, which makes it difficult to ensure each image in the training dataset is independent from others; in other words, the contribution of some samples can be replaced by others in the training process. However, it is hard to screen out which one may be redundant. The relatively  
Figure 3: Results of Liver segmentation using FCN model. (**A,B**) display the 3D liver segmentation result of the baseline and proposed a model, respectively; (**C,D**) display the hard samples liver segmentation results of the baseline and proposed a model, respectively; Blue and red lines in **C** and **D** denote ground truth and prediction results. Each row shows results acquired from an individual case. shallow network structure, which has relatively weak deep feature extraction capability, may be another reason for the phenomenon observed in this work. Meanwhile, the significantly reduced performance of the model in the case of a large reduction in training dataset also supports the point that the more data, the better the performance in deep learning. Additionally, the same slices may play different roles in different segmentation tasks. For example, the positive-hard samples in the liver segmentation task may be negative-easy ones in kidney or spleen segmentation. Therefore, it is difficult to classify samples with the traditional unsupervised method. Inspired by the pre-training strategy, we use a pre-training method as a sample classifier to classify hard samples and easy samples in training dataset. We obtained better performance from the model after adding the enhanced hard examples. Conceptualization, [PERSON] ([PERSON]) and B.F.; Data curation, Y.Z., Y.W. ([PERSON]), W.Y. and H.L. ([PERSON]); Funding acquisition, [PERSON] ([PERSON]); Writing-original draft, Y.Z. and Y.W. ([PERSON]); Writing-review & editing, H.L. ([PERSON]). This work was supported by the National Natural Science Foundation of China (61876026, 61672120) and The Social Livelihood Science and Technology Innovation Special Project of CSTC (no. CSTC2015 shmszx120002). The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dice Score**} \\ \cline{2-4}  & **Mean** & **Min** & **Max** \\ \cline{2-4}  & **Mean** & **Min** & **Max** \\ \hline selection ratio = 1, baseline & 0.9532 \(\pm\) 0.035 & 0.9017 & 0.9875 \\ selection ratio = 0.8 & 0.9501 \(\pm\) 0.031 & 0.908 & 0.9863 \\ selection ratio = 0.5 & 0.9486 \(\pm\) 0.023 & 0.898 & 0.9867 \\ selection ratio = 0.3 & 0.9107 \(\pm\) 0.057 & 0.834 & 0.9821 \\ selection ratio = 0.2 & 0.8932 \(\pm\) 0.063 & 0.796 & 0.9806 \\ proposed model & 0.9604 \(\pm\) 0.022 & 0.912 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Kidney segmentation results on test dataset based on different selection ratio using FCN model. \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 & 0.9167 \(\pm\) 0.13 & 0.8262 & 0.9698 \\ selection ratio = 0.8 & 0.9106 \(\pm\) 0.089 & 0.8213 & 0.9703 \\ selection ratio = 0.5 & 0.9078 \(\pm\) 0.114 & 0.822 & 0.9693 \\ selection ratio = 0.3 & 0.8835 \(\pm\) 0.035 & 0.7935 & 0.9687 \\ selection ratio = 0.2 & 0.8511 \(\pm\) 0.039 & 0.7724 & 0.9684 \\ proposed model & 0.9258 \(\pm\) 0.067 & 0.8547 & 0.9693 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Kidney segmentation results on test dataset based on different selection ratio using FCN model.