# An Improved V-Net Model for Thyroid Nodule Segmentation
## 2 Literature Review
[22] designed a frequency-domain enhancement network (FDE-Net), a U-Net-based network structure for accurately and rapidly detecting thyroid nodules. In the model, they preferred structures that enhanced the image contrast and reduced factors such as noise, negatively impacting the segmentation performance. The authors noted that their approach had a weak effect on the segmentation of multiple nodules. However, it has a good diagnostic effect for nodules of different sizes and possesses robust generalization capabilities compared with other models. They achieved a Dice score of 83.54% in thyroid nodule segmentation using FDE-Net. [PERSON] et al. [23] presented a computer-aided diagnostic model called transformer fusing CNN network (TCNet) for segmenting malignant thyroid nodules. They combined large-kernel CNNs and enhanced transformer architectures with a multi-scale fusion module (MFM) designed to create high-level feature maps. In experimental studies, they showed that their models achieved Dice scores of 82.65% and 86.42% for the thyroid datasets MTNS and TN-SCUI2020, respectively, and a Dice score of 84.89% for the GLAS colon histology dataset. They obtained IoU scores of 74.38%, 78.56%, and 80.02% for the MNTS, TN-SCUI2020, and GLAS datasets, respectively. [PERSON] et al. [8] proposed a new thyroid nodule segmentation model (FCGNet) developed from UNet3+ to create low computational cost feature maps. In the proposed model, they used a ghost module, ghost bottleneck, and squeeze-and-excitation (SE) blocks. The authors utilized two datasets for evaluation: the DDTI dataset and a combined comprehensive dataset formed by merging the DDTI with hospital data. They achieved highly successful results, with 95.40% accuracy and a Dice score of 80.42%on the DDTI dataset and 94.88% accuracy and a Dice score of 86.70% on the combined comprehensive dataset. They reported that their approach not only increased the accuracy of ultrasonic diagnosis but also reduced hardware and computational requirements. [PERSON] et al. [24] developed a shape-margin knowledge-augmented network (SkaNet) as an effective model for thyroid nodule segmentation and diagnosis. They evaluated the model using a CNN subnetwork and a transformer subnetwork on both an open-access dataset and a non-public dataset to obtain shared features. Using the open-access dataset, they achieved a Dice score of 84.98% for thyroid nodule segmentation and an accuracy of 98.06% for thyroid nodule diagnosis. In the non-public dataset, they reported a Dice score of 86.01% and an accuracy of 94.61%. An IoU of 73.88% for the DDTI dataset and an IoU of 75.45% for the non-public dataset were achieved. [PERSON] et al. [16] proposed a multi-task approach using an anomaly detection (AD) module to automatically detect and segment thyroid nodules from ultrasound images. They evaluated the impact of the AD module on various state-of-the-art nodule segmentation architectures using the UCLA, DDTI, and Stanford CINE datasets. Unlike previous studies, their study also included images without nodules during the evaluation. Among the evaluated models, SResUNET-AD, a U-Net with an ImageNet pre-trained encoder and AD achieved the highest F1 score of 83.9% on the test set and an across-the-image-widths Dice score of 80.8%. Utilizing the same dataset, they recorded their best results on positive images with MSUNet-AD, reaching a Dice score of 65.5% and an IoU of 56.5%. SResUNet-AD also demonstrated superior performance on both the DDTI and Stanford CINE datasets, with a Dice score of 40.2% and an IoU of 33.3% and a Dice score of 59.2% and an IoU of 51% for positive images. [PERSON] et al. [25] presented a study based on adversarial networks with multi-scale joint loss for thyroid nodule segmentation (TNSeg), which utilizes a segmentation block, a discriminative block, and adversarial training. They stated that accurate nodule segmentation from ultrasound images is necessary for effective planning of diagnosis and treatment and that existing approaches struggle due to intra-nodule variability.