# An Improved V-Net Model for Thyroid Nodule Segmentation
## 5 Conclusions  
Thyroid disorders, especially malignant nodular thyroid cancers, are becoming a significant health issue worldwide. Early diagnosis and treatment are crucial for reducing complications and shaping the disease course. Consequently, there is an increasing need for tools and methods that can provide accurate and reliable segmentation results from images obtained using advanced imaging technologies. This study proposes an improved V-Net approach that utilizes a triple fusion architecture and a channel attention mechanism to segment thyroid nodules effectively. The triple fusion architecture was implemented within the encoder section of the model, whereas SE mechanisms were incorporated across all encoder blocks, as well as in the last decoder block. By merging features scaled differently at each encoder layer through triple fusion architecture, we aim to enhance model performance by creating a rich representation of features. The SE mechanisms facilitate the focus on significant features based on their interrelationships within the \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 two input sets in the encoder With three input sets in the encoder (improved V-Net) & 0.8257 & 0.7382 & 5197.6772 & 0.8332 & 0.7529 & 19,320.5820 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of the encoder fusion structures with two and three input sets. encoder blocks. In addition, the SE mechanism employed in the last decoder block enables segmentation based on key features, thereby improving overall model effectiveness. In this study, the performances of the methods employed for thyroid nodule segmentation were tested on two distinct image datasets: DDTI and TN3K. Ablation studies, quantitative assessments, and qualitative observations were conducted to develop and test the proposed model. The results from the ablation experiments indicate that implementing architecture with three input sets in the encoder (triple fusion architecture) enhances performance in terms of the Dice and IoU metrics. Furthermore, the experiments conducted in the decoder section indicated that using the SE mechanism only in the last decoder block contributed more to the model's performance in terms of both training time and relevant metrics than using it in all blocks. Consequently, the proposed method for thyroid nodule segmentation was structured as an improved V-Net model, with the encoder section using triple fusion architecture incorporating SE mechanisms at each block and the decoder section only including the SE mechanism in the last block. In the experiments on quantitative assessments, the proposed model and the V-Net, fusion V-Net, and SEV-Net models used for the performance comparison were applied to both datasets, and performance analyses were conducted by comparing the Dice, IoU, and training time metrics. The findings revealed that the proposed model outperformed the others in terms of the Dice and IoU scores across both datasets. Nevertheless, due to having the longest training time among all the models, it turned out to be the one with the highest computational expense. The results obtained from experiments conducted on qualitative observations indicated that while the proposed model successfully produced results in segmenting small- and medium-sized nodules, its success rate decreased in segmenting large nodules. The results of the literature comparison conducted on the DDTI and TN3K datasets show that the proposed model achieves a performance that surpasses that of existing models in terms of the average scores obtained for Dice and IoU.