# An Improved V-Net Model for Thyroid Nodule Segmentation
## 2 Literature Review  
Like many other diseases, early detection of thyroid nodules plays a crucial role in preventing the condition from worsening to a point where it could significantly impair a patient's quality of life or pose a threat to their life. This is especially true for malignant nodules, for which timely intervention can greatly contribute to effective treatment. Over the years, extensive research has focused on contour-shape analysis, ML, and DL methodologies. Recently, attention has shifted towards DL-based approaches. Many of these studies have emphasized integrating DL techniques with alternative methods or enhancing them with functional modules to achieve successful segmentation outcomes. Below is a summary of the literature on DL applications in thyroid nodule segmentation. [PERSON] et al. [18] proposed a marker-guided U-Net segmentation (MGU-Net) approach based on manually determined information for the nodule diagnosis process. They used a non-public dataset containing 2246 thyroid nodules collected. The experimental results indicated that their method outperformed the other approaches, achieving a Dice score of 95.76% and an IoU success of 91.46%. They also evaluated their approach in terms of computational cost and found it to be more efficient than the methods they compared. [PERSON] et al. [5] developed a dual-path neural network called TNSNet based on the DeepLabV3+ backbone, which uses soft shape supervision to improve the detection of nodule boundaries and segmentation performance. They evaluated the developed network using a dataset of 3786 thyroid nodule ultrasound images. Their experimental studies achieved an accuracy of 95.81% and a Dice score of 85.33% using the TNSNet model. [PERSON] et al. [19] developed a weakly supervised semantic segmentation model that uses only image-level classification labels to extract the semantic features of benign and malignant nodules and creates class activation maps. They employed a dual-branch soft erase module (DSEM), a scale feature adaptation module (SFAM), and an edge self-attention module (ESAM). The proposed model achieved Dice scores of 60.2% for benign nodules and 71.3% for malignant nodules on a non-public dataset and Jaccard scores of 46.2% for benign nodules and 56.1% for malignant nodules. The model was validated through ablation studies and compared with the state-of-the-art (SOTA) methods. These results indicate that the model is a cost-effective approach for thyroid nodule segmentation. [PERSON] et al. [20] proposed the LCA-Net approach, which successfully uses a local feature information and global context information structure to segment nodule boundaries. In order to verify the model's success in addressing the segmentation challenges posed by weak edges in ultrasound images and the complexity of the thyroid tissue structure, they utilized the TN-SCUI2020 and TN3K datasets, which have different data sources and dataset sizes. Based on the experimental results, they reported that the LCA-Net model achieved a Dice score of 90.26%, 90.68% precision, and 91.84% recall for the TN-SCUI2020 dataset and a Dice score of 82.08%, 80.55% precision, and 85.34% recall for the TN3K dataset. They achieved a Jaccard score of 82.65% for the TN-SCUI2020 dataset and a Jaccard score of 71.18% for the TN3K dataset. [PERSON] et al. [21] developed an approach incorporating nested and attention-based networks to leverage the advantages of these structures for improving segmentation performance. They used a suggestion network (SN) and an enhancement network (EN) to construct a nested network. They noted that segmenting the thyroid region was challenging when the lesions were small or when significant lesions were present. They achieved more successful results than the other methods, with a Dice score of 61.2% for the DDTI dataset and 83.7% for the 3 DThyroid dataset. In the processing time analysis, they mentioned that the proposed method required a longer processing time than the other segmentation networks. [PERSON] et al.