## 5 Discussion
The new architecture was called the light dense (LD) model. In LD model, each depthwise convolution unit is densely connected with another depthwise convolution unit within the same light dense block (shown in Figure 6). This architecture incorporates feature propagation and boosts feature reuse while effectively reducing the number of parameters. Though the LD model obtained better segmentation accuracy (Avg. Dice score: 0.8204) with a fewer number of parameters, it was still unable to adequately distinguish the TBFs and LBFs and generated conjoined bony features in the output as the input images were noisy. The two issues, high noise in images and variability of bony features, were tackled separately as follows:  
(a) To improve the segmentation clarity and tackle the 'noisy' information, trainable selection gates were employed in the LD model as the next modification and the new architecture was called the light convolution dense selection (LCS) model. Through this modification, it was anticipated that the gating mechanism [43] would smartly suppress irrelevant information such as noise from the feature maps and be able to identify a greater number of bony features from the useful information in the image. Past research shows that the gating mechanism can be effectively used to extract the selective features from the noisy ultrasound images as it suppresses noise and enables the network to make segmentation predictions based on class-specific features [67]. In effect, the selection gate blocked extraneous information from information-rich feature maps and thereby improved the segmentation output for noisy ultrasound images (Avg. Dice score: 0.8292). (b) To handle the problem of large variabilities in shape, size, and locations of the TBFs and LBFs, the conventional skip-pathways were replaced by multi-scale [41] skip-paths and the architecture was named as the light-dense inception (LDI) model. Feature fusion was enhanced by using this skip-path as the inconsistencies between encoder-decoder features were bridged. This path solves the problem of the semantic gap between the encoder and decoder side and enhances the feature fusion and improves the segmentation output [41]. Through this modification, the low-level features underwent additional processing and the multi-scale inception layers, adapted in this skip-path, enabled the network to extract more features from different scales. This provided a significant boost to the segmentation performance (Avg. Dice score: 0.8419) compared to the initial LD model. As a final step, the two sub-models were combined into the final architecture and called the Light-Convolution Dense Selection U-Net (LDS U-Net) model. As anticipated, the combination of selection gates and multi-scale skip pathway gave a much improved segmentation accuracy (Avg. Dice score: 0.8694) compared to both the LD model and basic U-Net model. To understand the performance of LDS U-Net vis-a-vie other contemporary models, the proposed model was compared with the basic U-Net, MultiResUNet, and Attention U-Net using the same 109 volume projection ultrasound spine image datasets. First, LDS U-Net quantitatively outperformed the other models with 0.7415 Jaccard index, 0.8694 Dice coefficient, 0.8885 F1 score, and 0.9592 pixel accuracy. Second, it is able to better identify the thoracic and lumbar bony features most consistently when qualitatively compared to other models. The segmentation outcomes of LDS U-Net, through manual observation and measurement of distinguishable bony features, are also very promising. Third, as it is constructed using light dense block, LDS U-Net was also found to be computationally efficient with the least number of parameters and smallest program memory size. Finally, in handling noisy ultrasound images with high variability, the LDS U-Net was successful in correctly segmenting 76.59% of total images (shown in Table 3) with complete identification of all 26 bony features, which is more consistent than any of the other models.