# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 4 Experimental Results
### Experiments on Dataset-B  
#### 4.3.1 Selection of the Best Performing Algorithm on Dataset-B  
After choosing the right algorithm and the right configuration to use on Dataset-A, we pass to Dataset-B. We aim by experiments on this dataset to test the generalization of the model and to segment successfully new spinal cords that are not in the training set. To select the best performing algorithm, we run the five segmentation model already tested on Dataset-A and trained it on every dataset Dataset-B\({}^{i}\) for 400 epochs. After that, we measure the average on all measures to judge fairly the algorithm that has performed well on new spinal cords. As shown in Tables 8-12, U-Net [26] outperforms clearly the state of the art algorithms in this task. We deduce from the experiments that U-Net is able to learn complicated patterns from only a small sized data without memorizing the patterns existing in the train set. Hence, it will be the first choice algorithm to be adopted in the next steps of spinal cord analysis in ultrasound imagery. To improve moreover the performance of U-Net, we will cite in the next sub-section the right configuration to use. The integration of ASPP inside U-Net will not be tested following the conclusion we made on experiments made on Dataset-A. ASPP will not have a significant impact on accuracy because the patterns in our task do not have multiscale features to be learned. However, We will study the impact of the other modifications on U-Net (DSC, Post-processing, and set of training configurations). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **IoU** & **Dice Score** & **Accuracy** & **Recall** & **Precision** \\ \hline before & 0.946 & 0.989 & 0.989 & 0.989 & 0.989 \\ with post-processing & 0.953 & 0.991 & 0.991 & 0.991 & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Metrics measured before and after applying the post-processing step on the FC-DenseNet103 network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **U-Net** & **DenseNet-103** & **BiSeNet** & **PSPNet** & **DeepLab v3+** \\ \hline Dataset-B1 & 0.939 & 0.937 & 0.916 & 0.893 & 0.897 \\ Dataset-B2 & 0.874 & 0.872 & 0.809 & 0.901 & 0.852 \\ Dataset-B3 & 0.924 & 0.824 & 0.923 & 0.87 & 0.862 \\ Dataset-B4 & 0.94 & 0.907 & 0.801 & 0.808 & 0.833 \\ Dataset-B5 & 0.925 & 0.944 & 0.865 & 0.828 & 0.817 \\ Dataset-B6 & 0.901 & 0.819 & 0.91 & 0.785 & 0.835 \\ Dataset-B7 & 0.937 & 0.918 & 0.918 & 0.859 & 0.874 \\ Dataset-B8 & 0.961 & 0.957 & 0.905 & 0.897 & 0.92 \\ Dataset-B9 & 0.929 & 0.921 & 0.846 & 0.838 & 0.788 \\ Dataset-B10 & 0.933 & 0.906 & 0.87 & 0.844 & 0.803 \\ \hline Dataset-B (cross validation) & 0.9263 & 0.9005 & 0.8763 & 0.8523 & 0.8481 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: IoU measured for every algorithm on Dataset-B (cross-validation). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **U-Net** & **FC-DenseNet-103** & **BiSeNet** & **PSPNet** & **Deeplab v3+** \\ \hline Dataset-B1 & 0.986 & 0.986 & 0.98 & 0.977 & 0.978 \\ Dataset-B2 & 0.981 & 0.981 & 0.965 & 0.987 & 0.977 \\ Dataset-B3 & 0.987 & 0.967 & 0.985 & 0.979 & 0.98 \\ Dataset-B4 & 0.994 & 0.98 & 0.969 & 0.958 & 0.967 \\ Dataset-B5 & 0.987 & 0.99 & 0.977 & 0.962 & 0.965 \\ Dataset-B6 & 0.982 & 0.99 & 0.985 & 0.961 & 0.977 \\ Dataset-B7 & 0.986 & 0.981 & 0.98 & 0.966 & 0.97 \\ Dataset-B8 & 0.996 & 0.989 & 0.98 & 0.986 & 0.981 \\ Dataset-B9 & 0.983 & 0.99 & 0.971 & 0.958 & 0.957 \\ Dataset-B10 & 0.984 & 0.994 & 0.971 & 0.958 & 0.966 \\ \hline Dataset-B (cross validation) & 0.9866 & 0.9848 & 0.9763 & 0.9692 & 0.9718 \\ \hline \end{tabular}
\end{table} 
Table 11: Recall measured for every algorithm on Dataset-B (cross-validation).