# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion
This method demonstrated more successful segmentation results for larger nodules. Despite achieving satisfactory outcomes, with a Dice score of 82.08% and an IoU score of 71.18%, it yielded the lowest Dice score among those studied within this dataset. BPAT-UNet [17], designed to enhance feature extraction for accurately identifying thyroid nodules, is a method capable of detecting small thyroid nodule objects and recognizing blurry boundaries. The method produced a Dice score of 83.64%, making it the model that yielded results closest to the US-Net. The obtained IoU score of 71.87% reflects an acceptable level of effectiveness for thyroid nodule segmentation. The TRFE+ method [29] achieved a Dice score of 83.30 \(\pm\) 0.26% and an IoU score of 71.38 \(\pm\) 0.43% from the TN3K dataset, notably higher than those recorded on the DDTI dataset. Its Dice performance indicates that it produced results quite close to those of the AMSeg and improved V-Net methods, which are noted for their good performance in this metric. Another model that leveraged both the DDTI and TN3K datasets, AMSeg [43], achieved outstanding results with a Dice score of 84.21 \(\pm\) 0.21% and an IoU score of 72.48 \(\pm\) 0.38% on the TN3K dataset. When the results were compared with the results of the model in the DDTI dataset, it was observed that they were quite high compared to DDTI dataset. Following TNSeg, which is known to have the highest Dice scores, it provided the second-highest performance in terms of this metric. TNSeg [25] is a method implemented on both datasets, much like TRFE+ and AMSeg, with its performance results on the TN3K dataset being considerably better than those on the DDTI dataset. The Dice score of 85.71% obtained from TN3K stands out as the highest among all listed methods. Additionally, with an IoU score of 73.18%, this method has one of the best average performances, following the improved V-Net model. The results in this table also reveal that the proposed improved V-Net method does not yield vastly different performance outcomes in terms of Dice and IoU scores from both datasets compared with the previously mentioned methods; instead, it shows more balanced and closely aligned results. While achieving a Dice score of 83.88% on the TN3K dataset places it behind TNSeg and AMSeg in this regard, its score is notably close to that of these methods, particularly AMSeg. Regarding the IoU metric, this method performed better than the other methods, scoring 75.50%. It achieved the best performance, with a score of 79.69% when evaluating the results based on the average performance across both metrics. Meanwhile, TNSeg produced an average performance result close to that of the improved V-Net, at 79.45%. Figures 10 and 11 show qualitative comparisons of the nodule segmentation results from the methods applied to the DDTI and TN3K datasets, respectively. Compared with the manual segmentation results for nodule segmentation, the improved V-Net model produced results that were more closely aligned with the original segmentation masks. However, the proposed method faces challenges when segmenting larger nodule structures. In this study, ablation experiments were conducted to test how an improved V-Net model with either two or three input sets in the encoder affects the performance, as well as the effect of using the SE mechanism across all decoder blocks versus only applying it to the last block. Table 6 presents a comparison of the performance obtained in terms of Dice, IoU, and training time by applying the model that receives three input sets in the encoder with the SE mechanism added to all decoder blocks and the improved V-Net model on the DDTI and TN3K datasets. As can be seen from the training time results for both datasets in Table 6, adding the SE mechanism to all blocks of the decoder layer increased the number of parameters, resulting in longer training times than the improved V-Net model.