## 5 Discussion  
Being a radiation-free and economic imaging modality, the 3D ultrasound imaging system has the potential to become a very popular diagnosis technique to detect scoliosis [15]. However, this necessitated the establishment of new measurement indices as, in the X-ray and Cobb angle method, scanning is conducted to assess anterior spinal deformity. A new index called spinous process angle (SPA) was developed exclusively for ultrasound scanning to measure the posterior spinal deformity. In a nutshell, if lines are drawn through the most tilted parts of the spinous column profile of coronal ultrasound images, then the angle that is formed is known as the SPA. The SPA measurement focuses on the middle dark line of the spine ultrasound image as the main region of interest. Subsequently, several scoliosis assessment techniques, both manual and automatic, have been researched to measure SPA and to demonstrate its equivalence to the gold-standard Cobb angle [16; 18]. However, there is an overall drawback in the SPA measurement approach as there was a high inherent tendency of the technique to underestimate the severity of the curvature of scoliosis compared to the Cobb angle [1; 15]. Subsequently, an alternate index was developed called the ultrasound curvature angle (UCA). Unlike SPA, in the UCA technique, the lateral bony features are the main regions of interest and further research was undertaken to demonstrate that it is equivalent to the X-ray Cobb angle [20]. Though a manual method of UCA measurement is established [20], there is a need to find a suitable method to automate the UCA measurement so that the technique can be made fast and scalable. Clear demarcation of lateral bony features is a vital step in UCA measurement. In traditional X-ray, the images are relatively noise-free and clear features can be identified \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \
\begin{tabular}{c} **Total Number of** \\ **Parameters** \\ \end{tabular} &
\begin{tabular}{c} **Size in Megabytes (MB)** \\ \end{tabular} \\ \hline U-Net & 31 Mil & 355 MB \\ Attention U-Net & 37.1 Mil & 433.49 MB \\ MultiResUNet & 7.2 Mil & 146.01 MB \\
**Proposed LDS U-Net** & **9.1 Mil** & **127.5 MB** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of computational requirement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Truth Mask** & **U-Net** & **Attention U-Net** & **MultiResUNet** & **LDS U-Net** \\ \hline
9 pairs of TBF, T12 region, and & & & & \\
6 LBFS—Total: 26 bony & 25.11 & 25.23 & 25.30 & **25.45** \\ features & & & & \\ \% of images where all 26 & 69.72\% & 71.56\% & 73.23\% & **76.59\%** \\ features were fully detected & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between number of bony features identified (avg). easily. However, in the case of ultrasound modality, scanned images have low contrast, and are often plagued by speckle noise. This makes the differentiation of the bony features challenging. In addition, the fact that the lateral bony features are numerous and can significantly vary in shape, size, and location, adds to the overall complexity of the problem. The first step of automatic UCA measurement is clear segmentation of the lateral bony features and this paper attempted to accomplish this first step of image segmentation using deep learning. U-Net is one of the most successful architectures for biomedical image processing. As a starting point, the ultrasound spine images were segmented using basic U-Net [26]. However, the segmentation output was not satisfactory (Avg. Dice score: 0.8133) as the output images had many missed or conjoined bony features and the architecture required a large number of parameters, which directly increased the computation cost and memory size [66]. As the first modification, the conventional convolution layers of U-Net were replaced with proposed light dense block and depthwise convolutional operations that replaced the conventional convolution operations.