# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 5 Conclusions  
In this study, we provided a solution to the problem of segmentation of the spinal cord in ultrasound imaging. This is done based on the state of the art algorithms existing in semantic segmentation. We constructed two datasets (Dataset-A and Dataset-B). Dataset-A tests the performance of the algorithm on spinal cord patterns already provided in the train set. Dataset-B tests the performance of the algorithm on new spinal cord patterns that are not provided in the train set in a cross-validation way. On Dataset-A, FC-DenseNet103 outperforms all the state of the art methods due to its capability to learn complex data patterns using the DenseNet Block. On Dataset-B, U-Net is the best due to its ability to learn complex patterns from a limited size of data without memorizing exactly the train set patterns. We found that the integration of the Atrous Spatial Pyramid Pooling module did not improve the performance of the model on our task. This is probably because the spinal cord in ultrasound images has a small variance in size and does not have multi-scale features to be captured using ASPP. We found that the Depthwise Separable Convolution (DSC) significantly reduces the size of the model, and the computations needed to train it without affecting the performance. We demonstrated that the post-processing step has a significant impact on improving the segmentation accuracy. We also concluded some configurations to be used during the training to fine-tune the model for best performance. Our work is the first step towards the automatic analysis of the spinal cord using intra-operative ultrasound medical imaging. The next task will be to automatically extract the pulsation curve of the spinal cord from the ultrasound video. This is useful to better analyze the movement of the spinal cord during the laminectomy operation and confirm the extent of decompression.  
[PERSON] designed the idea, implemented the experiments and wrote the paper [PERSON] supervised the work. [PERSON] contributed to the idea and implemented some experiments. [PERSON] helped in clinical data construction and analysis. [PERSON]-[PERSON] contributed to the idea and supervised the clinical context of the paper. [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by (Prince Sultan University) and (Raytheon Chair for System Engineering). The authors are grateful to (Prince Sultan University) and to (Raytheon Chair for System Engineering) for funding. The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **U-Net (Before)** & **U-Net (After)** \\ \hline Dataset-B1 & 0.939 & 0.941 \\ Dataset-B2 & 0.874 & 0.899 \\ Dataset-B3 & 0.924 & 0.953 \\ Dataset-B4 & 0.94 & 0.936 \\ Dataset-B5 & 0.925 & 0.936 \\ Dataset-B6 & 0.901 & 0.928 \\ Dataset-B7 & 0.937 & 0.95 \\ Dataset-B8 & 0.961 & 0.969 \\ Dataset-B9 & 0.929 & 0.917 \\ Dataset-B10 & 0.933 & 0.947 \\ \hline \hline Dataset-B (cross validation) & 0.9263 & 0.9376 \\ \hline \hline \end{tabular}
\end{table}
Table 15: IoU tested on Dataset-B using U-Net before and after using the chosen configurations.