# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Experiments
#### 2.9.2. Performances of Models on Segmentation of Brain Tumors in BraTS Dataset  
To compare the variables contributing to the performances of the models trained with the NTUH dataset, we experimented with the segmentation of the brain tumors in the BraTS dataset. We trained our models with either 4-channel or only T1+C inputs to compare the performances of the models trained with the same imaging modality in the NTUH dataset. During training, the labels encompassed either five classes or only tumor cores. The segmentation performances were measured using the hard dice. The evaluation was based on predictions of the tumor cores.  
Figure 1: _Cont_.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Numbers of** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Batch** \\ **Samplers** \\ \end{tabular} & **Loss Function** & **Precision** & **Sensitivity** & **Hard Dice** \\ \hline DeconvNet & 12,544,324 & two\_dim & \begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & 0.46 & 0.48 & 0.43 \\ U-Net & 34,524,034 & two\_dim & \begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & **0.48** & 0.48 & 0.43 \\ PSPNet & 28,280,773 & two\_dim & \begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & 0.47 & 0.48 & 0.43 \\ V-Net & 8,232,274 & uniform\_patch3d & \begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & 0.39 & 0.54 & 0.41 \\ V-Net & 8,232,274 & three\_dim & \begin{tabular}{c} Cross-entropy \\ log(soft-dice) \\ \end{tabular} & 0.2 & 0.56 & 0.25 \\ V-Net & 8,232,274 & three\_dim & \
\begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & **0.48** & 0.51 & 0.46 \\ V-Net dropout & 8,232,274 & three\_dim &
\begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & 0.47 & **0.66** & **0.51** \\
0.1 & 1,301,478 & center\_patch3d & \
\begin{tabular}{c} Cross-entropy minus \\ log(soft-dice) \\ \end{tabular} & 0.36 & 0.43 & 0.35 \\ DeepMedic & 1,301,478 & center\_patch3d &
\begin{tabular}{c} Cross-entropy \\ log(soft-dice) \\ Cross-entropy \\ \end{tabular} & 0.37 & 0.43 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of different models on the NTUH dataset.  
As shown in Figure 2, lesions with smaller target volumes introduce lower average dice performance for each deep-learning model. V-Net, the best-performing model in the current study, obtained a fairly satisfactory dice score when lesion size exceeded the median size of all targets.  
To compare the performances of different models trained with one-channel input on the segmentation of brain lesions, we performed another experiment in which models were trained with just T1+C input with tumor core labels of the BraTS dataset. The evaluation was based on prediction of the tumor cores. As shown in Figure 3, V-Net had the highest Dice score when trained with 4-channel input with 5-class labels. Interestingly, all models performed better in this circumstance than when trained with only T1+C images. Of note, V-Net and PSPNet could not yield comparable results when trained with only T1+C images, implying that they are more sensitive to the change from multimodal to single modality inputs. While the models trained with one-channel inputs yielded lower performances in segmentation, they still performed better than their counterparts trained with the NTUH dataset.  
Figure 1: Bar chart results of (**A**) dice score; (**B**) sensitivity; and (**C**) precision of deep learning-based segmentation versus ground truth on different lesion types in the NTUH dataset.  
Figure 2: The performance of each model on lesions within different volume ranges.  
Because of the nature of PSPNet and DeepMedic, they took a significantly longer time for inference, as shown in Table 6. V-Net had the least number of parameters and the shortest inference time. We also found that adding dropouts in V-Net further improved its performance, which we have noted in the table with 0.1 being the dropout rate.