# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion
Furthermore, when assessing both models' performances based on Dice and IoU metrics, it is evident from the table results that including SE mechanisms across all decoder blocks is a good predictor of the performance. Figure 10: Qualitative comparison of nodule segmentation results of different methods using visualization for the DDTI dataset. blocks negatively affected the performance. Consequently, the proposed improved V-Net was developed using an SE mechanism that was added solely to the last decoder block. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**DDTI**} & \multicolumn{3}{c}{**TN3K**} \\ \cline{2-7}  & **Dice** & **IoU** & \
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & **Dice** & **IoU** &
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} \\ \hline With SE & & & & & & \\ mechanisms in all & 0.8326 & 0.7404 & 7445.8562 & 0.8355 & 0.7539 & 27,132.8646 \\ Decoder layers & & & & & \\ With improved & 0.8451 & 0.7627 & 7286.6776 & 0.8388 & 0.7550 & 26,957.3628 \\ V-Net & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of integrating the SE mechanisms across all decoder blocks. Figure 11: Qualitative comparison of nodule segmentation results of different methods using visualization for the TN3K dataset. Table 7 illustrates the performance of the improved V-Net in terms of Dice, IoU, and training time metrics for the DDTI and TN3K datasets, depending on whether it receives two or three input sets in the encoder. The results from ablation experiments indicated that using a triple fusion encoder configuration led to improvements of 1.94% in Dice score and 2.45% in IoU for the DDTI dataset, along with enhancements of 0.56% in Dice score and 0.21% in IoU for the TN3K dataset, demonstrating that this model (improved V-Net) achieves superior segmentation results for thyroid nodules. The findings from both quantitative and qualitative assessments derived from experimental studies, along with the literature comparison results, showed that the proposed improved V-Net approach, bolstered by a triple encoder fusion architecture and SE mechanisms, can effectively facilitate the successful segmentation of thyroid nodules. Therefore, this model can serve as a valuable tool for radiologists and healthcare professionals when integrated into actual workflows. To facilitate implementation, it is essential to convert the model into an accessible tool suitable for clinical environments. This conversion can be accomplished through a user-friendly interface that works with imaging devices via a database, making the results generated by the model comprehensible to the radiologists. Radiologists can readily interpret the segmentation results generated by the model through automatic markings that are directly applied to images via the interface. For the effective operation of this interface program within a clinical environment, it is essential to have a computer equipped with a GPU and high-performance hardware to ensure reliable functionality. Furthermore, optimizing the model using various images sourced from the database can significantly boost its performance. However, one of the primary challenges hindering the applicability of the model in this domain is the consent issues surrounding patient data privacy and ethics.