# An Improved V-Net Model for Thyroid Nodule Segmentation
## 4 Experimental Results and Discussion  
Experimental studies of the improved V-Net-based method proposed for thyroid nodule segmentation and the V-Net, fusion V-Net, and SEV-Net methods used to verify its performance in this study were carried out on a local computer with an Intel(R) Core (TM)  
Figure 8: The area graph of the Dice similarity coefficient. i7-12700 KF CPU and NVIDIA Geforce GTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). Models for these methods were developed using Python (version 3.10.14) programming alongside TensorFlow and Keras libraries. The configuration of the models utilized in the experimental research has been outlined as follows: The V-Net consists of a 5-layer encoder and a 4-layer decoder. The fusion V-Net includes two 5-layer encoders along with one 4-layer decoder. SEV-Net features a 5-layer encoder, a 4-layer decoder, and SE mechanisms. The improved V-Net comprises three 5-layer encoders, one 4-layer decoder, and SE mechanisms. Each block of these models contains either two or three convolutional layers. In each block of the encoder layers, 3 \(\times\) 3 convolutional filters were used, and batch normalization was applied to their outputs. The results were passed through the ReLU activation function, followed by dropout, to prevent overfitting. A convolutional layer was implemented to prevent losing high-level features within the encoder layers instead of the maximum pooling layers typical in U-Net structures. Each block within the decoder layer effectively employs deconvolution to double the feature-map dimensions. Similar to the process in the encoder layer blocks, these layer blocks apply batch normalization, pass through ReLU activation functions, and sequentially undergo dropout on their 3 \(\times\) 3 convolution outputs. Residual connections were used in the models to facilitate learning information from the previous blocks. At the end of the decoder blocks, a convolutional layer with a 1 \(\times\) 1 size and a sigmoid activation function was employed for detecting masks and backgrounds. In the improved V-Net model, the squeeze ratio of the SE mechanisms was set to 4. These blocks were implemented in each block during the encoder phase, whereas in the decoder phase, they were added only to the last decoder block before the output. The definition and pre-processing steps used to perform the experiments on the DDTI and TN3K datasets are presented in the Section 3; initially, a split ratio of 0.8 was applied to create training (80%) and testing (20%) image collections. Following this, an additional split ratio of 0.2 was utilized on the existing training image set to obtain validation and training image sets for the experiments. Data augmentation techniques were employed in the final training set to strengthen the generalization ability of the model. This made the DDTI dataset six times more prominent and the TN3K dataset four times larger. The data augmentation process included applying random rotations, shifts, horizontal flips, zooming in/out, and angled shifts to the training samples. Consequently, the amounts of training, testing, and validation data used in the experiments were 2436, 127, and 102 for the DDTI dataset and 8940, 699, and 559 for the TN3K dataset, respectively. The other parameter settings were as follows: epoch number = 100, batch size = 4, learning rate = 10\({}^{-4}\), and dropout = 0.3. This study evaluated the performance of the segmentation models using quantitative and qualitative metrics. For quantitative assessment, changes in loss values across epochs and variations in Dice scores were analyzed during the training process, while standard measures, including Dice and IoU, were employed during the testing process. For qualitative assessment, the images predicted by the models were visually compared with the ground truth masks. Figure 9a,b show graphs of the changes in training and validation losses and Dice scores by epoch for the proposed method on the DDTI and TN3K datasets.