# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion
The AD module-based method [16] presents a novel multitask approach for detecting and segmenting nodules from ultrasound images. However, when evaluated against the other methods presented here, the performance results on the DDTI dataset were notably low for both metrics. Specifically, this method recorded Dice and IoU scores of 40.2% and 33.3%, respectively. The method, built on a suggestion and enhancement network architecture [21], incorporates a structure comprising nested attention mechanism networks. With a Dice score of 61.2% on the DDTI dataset, this method recorded the lowest performance for this metric following the AD module-based approach. US-Net [47] was successfully applied to the TN3K and DDTI datasets, achieving an impressive Dice score of 81.90 \(\pm\) 1.68%. In addition, it attained an IoU score of 70.39 \(\pm\) 1.35%. The proposed method produced a value close to that of Ska-Net, which achieved the highest score, with a Dice score of 84.51%. The IoU metric achieved a score of 76.27%, ranking it as the second-best performer after GLFNet. When assessing the results based on average performance across both Dice and IoU metrics, the improved V-Net method emerged as the top performer among those listed, with an average score of 80.39%. Ska-Net produced an impressive average performance score of 79.43%, which is closely aligned with that of the proposed method. GLFNet, which provided the highest performance in the IoU metric, fell behind both methods, with an average score of 77.10%. In recent studies using the TN3K dataset, as in studies related to thyroid nodule segmentation with the DDTI dataset, the methodologies applied represent enhanced and innovative approaches based on DL. Table 5 compares the results of DL-based studies in the literature using the TN3K dataset with those of the improved V-Net method. Enhanced-TransUnet [14], developed to address the blurry boundaries of images and assist in detecting small nodules, improves segmentation performance by integrating a score matrix with skip connections and enhancing the properties of the skip connections. The acceptable Dice score of 82.92% and IoU score of 70.87% achieved by this method on the TN3K dataset, along with similar scores for these metri \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Model** & **Dice (\%)** & **IoU (\%)** & \
\begin{tabular}{c} **(Dice +** \\ **IoU)/2 (\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [14] &
\begin{tabular}{c} Enhanced- \\ TransUnet \\ \end{tabular} & 82.92 & 70.87 & 76.90 \\ [PERSON] et al. [47] & US-Net & 83.66 \(\pm\) 0.46 & 72.92 \(\pm\) 0.38 & 78.29 \(\pm\) 0.42 \\ [PERSON] et al. [20] & LCA-Net & 82.08 & 71.18 & 76.63 \\ [PERSON] et al. [17] & BPAT-UNET & 83.64 & 71.87 & 77.76 \\ [PERSON] et al. [25] & TNSeg & **85.71** & 73.18 & 79.45 \\ [PERSON] et al. [29] & TRFE+ & 83.30 \(\pm\) 0.26 & 71.38 \(\pm\) 0.43 & 77.34 \(\pm\) 0.35 \\ [PERSON] et al. [43] & AMSeg & 84.21 \(\pm\) 0.21 & 72.48 \(\pm\) 0.38 & 78.35 \(\pm\) 0.30 \\
**[PERSON] and Atacak** & **Improved V-Net** & 83.88 & **75.50** & **79.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the performance of DL-based studies utilizing the TN3K dataset for thyroid nodule segmentation in the literature with that of the improved V-Net approach. demonstrate that vision transformer (ViT) models are scalable when dealing with large volumes of data. US-Net [47], presented to improve tissue analysis, feature selection, and edge detection in the complex challenges of medical imaging, achieved a score very close to the improved V-Net, with an average Dice performance score of 83.66%. The score of 72.92% obtained in terms of the IoU metric can be considered a successful performance. LCA-Net [20], a local and context attention adaptive network for thyroid nodule segmentation, is designed to capture edge information through various modules, obtain more global relational information, enhance local features, and detect nodules of different locations and sizes.