# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion
The thyroid region prior-guided feature-enhancement network (TRFE+) [29], a multi-tasking learning framework, simultaneously determines the nodule size, nodule and gland position while minimizing errors in classifying non-thyroid regions as thyroid nodules. While it performs well in scenarios involving multiple nodules and thyroid gland segmentation, it recorded a Dice score of 75.37 \(\pm\) 2.14% for thyroid nodule segmentation using the DDTI dataset. The global-local fusion network (GLFNet) [44] enhances segmentation by merging global semantic insights with local details; however, it underperforms compared to most methods in the table in terms of Dice score despite achieving an IoU score of 79.58%. MEF-UNet [45], an end-to-end multi-scale feature-extraction and fusion network, captures morphological and lesion characteristics, but it is the second-lowest-performing method among those presented, with an IoU score of 57.62%. The multifrequency and multiscale interactive CNN-transformer hybrid network (MFMSNet) [46] guarantees a more accurate segmentation performance by providing a low computational cost with octave convolutions while effectively utilizing the transformer structure to capture long-range dependencies. The findings from two publicly accessible breast ultrasound datasets (BUSI and BUI), along with one thyroid ultrasound dataset (DDTI), indicate that MFMSNet achieved a Dice score of 79.96% and an IoU score of 70.20% for the DDTI dataset, demonstrating acceptable performance in segmenting thyroid nodules. FCG-Net [8], developed to create feature maps with less computation, achieved more successful qualitative results for large nodules than the improved V-Net; however, it achieved a Dice metric score of 80.42%, falling short by 4.09% against the recommended model's performance. The multi-scale adversarial strategy AMSeg [43], based on Swin-Unet, effectively detects well-defined edges while \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dice (\%)**} & \multirow{2}{*}{**IoU (\%)**} & \multicolumn{2}{c}{**(Dice + IoU)/2 (\%)**} \\ \hline [PERSON] et al. [29] & TRFE+ & 75.37 \(\pm\) 2.14 & 60.47 \(\pm\) 1.08 & 67.92 \(\pm\) 1.61 \\ [PERSON] et al. [43] & AMSeg & 74.81 \(\pm\) 2.07 & 60.89 \(\pm\) 0.97 & 67.85 \(\pm\) 1.52 \\ [PERSON] et al. [44] & GLFNet & 74.62 & **79.58** & 77.10 \\ [PERSON] et al. [45] & MEF-UNet & 69 & 57.62 & 63.1 \\ [PERSON] et al. [46] & MFMSNet & 79.96 & 70.20 & 75.08 \\ [PERSON] et al. [8] & FCG-Net & 80.42 & - & - \\ [PERSON] et al. [24] & Ska-Net & **84.98** & 73.88 & 79.43 \\ [PERSON] et al. [25] & TNSeg & 74.93 & 61.11 & 68.02 \\ Radhachandran & Anomaly Detection & 40.2 & 33.3 & 36.75 \\ et al. [16] & (AD) Module & & & \\  & Method based on & & & \\ [PERSON] et al. & Suggestion and & & & \\
[21] & Enhancement & 61.2 & - & - \\  & Networks & & & \\ [PERSON] et al. [47] & US-Net & 81.90 \(\pm\) 1.68 & 70.39 \(\pm\) 1.35 & 76.15 \(\pm\) 1.52 \\
**[PERSON] and** & **Improved V-Net** & & & \\
**Atacak** & **(Proposed model)** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance of DL-based studies utilizing the DDTI dataset for thyroid nodule segmentation in the literature with that of the improved V-Net approach. enhancing edge pixel density but shows limited efficacy on smaller datasets. Ska-Net [24] is an effective network approach that enhances shape and margin knowledge augmented network for thyroid nodule segmentation and diagnosis. It attained the highest Dice score of 84.98% on the DDTI dataset among all the methods presented here, although its IoU score was recorded at 73.88%. TNSeg [25] introduces an innovative strategy that leverages adversarial networks with multi-scale joint losses and employs a discriminative block and a segmentation block, achieving a Dice score of 74.93% and an IoU score of 61.11%, reflecting the average performance compared to existing methods.