Thyroid Nodule Segmentation Research Articles

Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks

Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods.

H-Net: A dual-decoder enhanced FCNN for automated biomedical image diagnosis

Skin lesions and thyroid cancer have become diseases with a high incidence. The computer-aided diagnosis (CAD) system for dermatological diseases offers one of the most remarkable performance where deep learning technologies demonstrate their superiority in surpassing human experts. For developing a CAD system, a critical step is skin lesions and thyroid nodules diagnosis from dermoscopic images and ultrasound images, respectively. Although notable successes have been obtained using deep convolutional neural network (DCNN) models, several challenges hamper the practical applications in clinical due to the complexity of clinical data, e.g., skin lesions or thyroid nodules are irregular shapes or low contrast. To alleviate these issues, we propose a novel dual encoder-decoder network, called H-Net, for automated thyroid nodule and skin lesion segmentation. Specifically, a shallow CNN is applied at its left to learn the low-level details information called L-Net and a deep CNN is employed at its right to capture the high-level information called R-Net. Furthermore, to transfer information between the L-Net and the R-Net mutually, we propose a novel crossed skip connection strategy, which is a specific reliability skip connection way. In addition, to enhance the representation learning ability of the proposed pipeline, we propose a novel contextual information encoding module, which replaces conventional convolutional layers in H-Net. Meanwhile, we propose a novel hybrid loss to alleviate the imbalance training problem. To validate the effectiveness of H-Net, 600 pairs of dermoscopic images and 139 pairs of ultrasound images have been used for evaluation in experiments. Seven latest biomedical image segmentation approaches are compared, and ten metrics are utilized to evaluate the segmentation performance. Extensive experimental results demonstrate that our H-Net yields the new record, achieving a mIoU value of 84.8% on the ISIC-2017 dataset and a mIoU value of 87.5% on the TNUI-2021 dataset, which outperforms state-of-the-art approaches in both visual comparisons and quantitative evaluation. The codes are available at https://github.com/zxg3017/H-Net.

N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation.

Medical image segmentation is an essential component of computer-aided diagnosis (CAD) systems. Thyroid nodule segmentation using ultrasound images is a necessary step for the early diagnosis of thyroid diseases. An encoder-decoder based deep convolutional neural network (DCNN), like U-Net architecture and its variants, has been extensively used to deal with medical image segmentation tasks. In this article, we propose a novel N-shape dense fully convolutional neural network for medical image segmentation, referred to as N-Net. The proposed framework is composed of three major components: a multi-scale input layer, an attention guidance module, and an innovative stackable dilated convolution (SDC) block. First, we apply the multi-scale input layer to construct an image pyramid, which achieves multi-level receiver field sizes and obtains rich feature representation. After that, the U-shape convolutional network is employed as the backbone structure. Moreover, we use the attention guidance module to filter the features before several skip connections, which can transfer structural information from previous feature maps to the following layers. This module can also remove noise and reduce the negative impact of the background. Finally, we propose a stackable dilated convolution (SDC) block, which is able to capture deep semantic features that may be lost in bilinear upsampling. We have evaluated the proposed N-Net framework on a thyroid nodule ultrasound image dataset (called the TNUI-2021 dataset) and the DDTI publicly available dataset. The experimental results show that our N-Net model outperforms several state-of-the-art methods in the thyroid nodule segmentation tasks.

Boundary regression-based reep neural network for thyroid nodule segmentation in ultrasound images

Boundary regression-based reep neural network for thyroid nodule segmentation in ultrasound images

Local and Context-Attention Adaptive LCA-Net for Thyroid Nodule Segmentation in Ultrasound Images.

The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with , convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics.

Thyroid nodule segmentation and classification in ultrasound images through intra- and inter-task consistent learning.

Thyroid nodule segmentation and classification in ultrasound images are two essential but challenging tasks for computer-aided diagnosis of thyroid nodules. Since these two tasks are inherently related to each other and sharing some common features, solving them jointly with multi-task leaning is a promising direction. However, both previous studies and our experimental results confirm the problem of inconsistent predictions among these related tasks. In this paper, we summarize two types of task inconsistency according to the relationship among different tasks: intra-task inconsistency between homogeneous tasks (e.g., both tasks are pixel-wise segmentation tasks); and inter-task inconsistency between heterogeneous tasks (e.g., pixel-wise segmentation task and categorical classification task). To address the task inconsistency problems, we propose intra- and inter-task consistent learning on top of the designed multi-stage and multi-task learning network to enforce the network learn consistent predictions for all the tasks during network training. Our experimental results based on a large clinical thyroid ultrasound image dataset indicate that the proposed intra- and inter-task consistent learning can effectively eliminate both types of task inconsistency and thus improve the performance of all tasks for thyroid nodule segmentation and classification.

Segmentation of thyroid nodules from ultrasound images using convolutional neural network architectures

Thyroid nodule segmentation is an indispensable part of the computer-aided diagnosis of thyroid nodules from ultrasound images. However, it remains challenging to segment the nodules from ultrasound images due to low contrast, high noise, diverse appearance, and complex thyroid nodules structure. So, it requires high clinical experience and expertise for proper detection of nodules. To alleviate the doctor’s tremendous effort in the diagnosis stage, we utilized several convolutional neural network architectures based on Encoder-Decoder architecture, U-Net architecture, Res-UNet architecture. To handle the complexity of the residual blocks, we also proposed three hybrid Res-UNet architectures by reducing the number of residual connections. The experimental analysis of the segmentation models proves the viability of residual learning in the U-Net architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to Res-UNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and U-Net can accurately delineate nodules without human intervention. This model helps to reduce dependencies on operators and acts as a decision tool for the radiologist.

DMU-Net: Dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation

It is meaningful for radiologists to segment thyroid nodules in ultrasound images quickly and accurately using an effective segmentation algorithm. With the rise of deep learning in computer vision, many deep learning-based methods have been proposed to assist radiologists in diagnosing thyroid diseases, such as thyroid nodule classification, detection and segmentation, but there exist few methods paying attention to malignant thyroid nodule segmentation. The goal of thyroid nodule segmentation is to identify the type of thyroid nodule. However, the identification of thyroid nodule type has been relatively well developed and the identification work almost can’t bother radiologists. The more important for radiologists is to detect the inconspicuous malignant nodules precisely in ultrasonic images, avoiding radiologists confusing tissues and malignant thyroid nodules during their diagnosis. This paper proposes a deep learning-based CAD (Computer-aided diagnosis) method called Dual-route Mirroring U-Net (DMU-Net) to segment malignant thyroid nodules automatically. The method uses two subnets (U-shape subnet, inversed U-shape subnet) and three modules (pyramid attention module (PAM), margin refinement module (MRM), aggregation module (AM)) to extract contextual information of thyroid nodules and margin details in ultrasonic images. Further, the strategy of mutual learning is introduced from the natural image classification task to enhance the performance of DMU-Net. We train and evaluate our method on the self-built Malignant Thyroid Nodule Segmentation (MTNS) dataset. Finally, we compare the DMU-Net with several classical deep learning-based methods on the MTNS dataset and other public datasets. The results show our DMU-Net can achieve superior performance on these datasets.

EANet: Iterative edge attention network for medical image segmentation

Accurate and automatic segmentation of medical images can greatly assist the clinical diagnosis and analysis. However, it remains a challenging task due to (1) the diversity of scale in the medical image targets and (2) the complex context environments of medical images, including ambiguity of structural boundaries, complexity of shapes, and the heterogeneity of textures. To comprehensively tackle these challenges, we propose a novel and effective iterative edge attention network (EANet) for medical image segmentation with steps as follows. First, we propose a dynamic scale-aware context (DSC) module, which dynamically adjusts the receptive fields to extract multi-scale contextual information efficiently. Second, an edge-attention preservation (EAP) module is employed to effectively remove noise and help the edge stream focus on processing only the boundary-related information. Finally, a multi-level pairwise regression (MPR) module is designed to combine the complementary edge and region information for refining the ambiguous structure. This iterative optimization helps to learn better representations and more accurate saliency maps. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods in four different challenging medical segmentation tasks, including lung nodule segmentation, COVID-19 infection segmentation, lung segmentation, and thyroid nodule segmentation. The source code of our method is available at https://github.com/DLWK/EANet

A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images.

Accurate recognition of medullary thyroid carcinoma (MTC) is of great importance in medical diagnosis, as MTC is rare but second-most malignant thyroid cancers with a high case-fatality ratio.1 But there is a lower recognition rate on distinguishing MTC from other thyroid nodules in ultrasound images, even by experienced experts. This paper introduces the computer-aided method to tackle the challenge of recognizing MTC from ultrasound images, including limited MTC samples, and ambiguities among MTC, benign nodules, and papillary thyroid carcinoma (PTC). The recognition of MTC based on large MTC samples of ultrasound images has never been explored, as only one existing work presented a relevant dataset with a limited 21 MTC samples. This study proposes a novel method for primarily differentiating MTC samples from benign nodules and PTC that is the most common thyroid cancer. Our method is a two-stage schema with two important components including a cascaded coarse-to-fine segmentation network and a knowledge-based classification network. The cascaded coarse-to-fine segmentation network incorporates two U-Net++ networks for improving the segmentation results of thyroid nodules. Meanwhile, our knowledge-based classification network extracts and fuses semantic features of solid tissues and calcification for better recognizing the segmented nodules from the ultrasound images. In our experiments, dice similarity coefficient (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) are adopted for evaluating the segmentation results of thyroid nodules, and accuracy, precision, recall, and F1-score are used for classificationevaluation. We present a well-annotated dataset including samples of 248 MTC, 240 benign nodules, and 239 PTC. For thyroid nodule segmentation, our designed cascaded segmentation network attains values of 0.776 DSC, 0.689 IoU, 0.778 precision, and 0.821 recall, respectively. By incorporating prior knowledge, our method achieves a mean accuracy of 82.1% in classifying thyroid nodules of MTC, PTC, and benign ones. Especially, our method gains the higher performance in recognizing MTC with an accuracy of 86.8%, compared to nearly 70% diagnosis accuracy of experienced doctors. The experimental results on our Fujian Provincial Hospital dataset further validate the efficiency of our proposedmethod. Our proposed two-stage method incorporates pipelines of thyroid nodules segmentation and classification of MTC, individually. Quantitative and qualitative results indicate that our proposed model achieves accurate segmentation of thyroid nodules. The results also validate that our learning-based framework facilitates the recognition of MTC, which gains better classification accuracy than experienced doctors.

TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision

Background and objectives: Thyroid nodules are a common disorder of the endocrine system. Segmentation of thyroid nodules on ultrasound images is an important step in the evaluation and diagnosis of nodules and an initial step in computer-aided diagnostic systems. The accuracy and consistency of segmentation remain a challenge due to the low contrast, speckle noise, and low resolution of ultrasound images. Therefore, the study of deep learning-based algorithms for thyroid nodule segmentation is important. This study utilizes soft shape supervision to improve the performance of detection and segmentation of boundaries of nodules. Soft shape supervision can emphasize the boundary features and assist the network in segmenting nodules accurately. Methods: We propose a dual-path convolution neural network, including region and shape paths, which use DeepLabV3+ as the backbone. Soft shape supervision blocks are inserted between the two paths to implement cross-path attention mechanisms. The blocks enhance the representation of shape features and add them to the region path as auxiliary information. Thus, the network can accurately detect and segment thyroid nodules. Results: We collect 3786 ultrasound images of thyroid nodules to train and test our network. Compared with the ground truth, the test results achieve an accuracy of 95.81% and a DSC of 85.33. The visualization results also suggest that the network has learned clear and accurate boundaries of the nodules. The evaluation metrics and visualization results demonstrate the superior segmentation performance of the network to other classical deep learning-based networks. Conclusions: The proposed dual-path network can accurately realize automatic segmentation of thyroid nodules on ultrasound images. It can also be used as an initial step in computer-aided diagnosis. It shows superior performance to other classical methods and demonstrates the potential for accurate segmentation of nodules in clinical applications.

Stiffness prediction on elastography images and neuro-fuzzy based segmentation for thyroid cancer detection.

The elastography method detects metastatic changes by measuring the stiffness of tissues. Estimation of elasticities from elastography images facilitates more precise identification of the metastatic region and detection of the same. In this study, an automated segmentation algorithm is proposed that calculates pixel-wise elasticity values to detect thyroid cancer from elastography images. This intensity to elasticity conversion is achieved by constructing a fuzzy inference system using an adaptive neuro-fuzzy inference system supported by two meta-heuristic algorithms: genetic algorithm and particle swarm optimization. Pixels of the input color images (red, green, and blue) are replaced by equivalent elasticity values (in kilo Pascal) and are stored in a two-dimensional array to form an "elasticity matrix." The elasticity matrix is then segmented into three regions, namely, suspicious, near-suspicious, and non-suspicious, based on the elasticity measures, where the threshold limits are calculated using the fuzzy entropy maximization method optimized by the differential evolution algorithm. Segmentation performances are evaluated by Kappa and the dice similarity co-efficient, and average values achieved are 0.94±0.11 and 0.93±0.12, respectively. Sensitivity and specificity values achieved by the proposed method are 86.35±0.34% and 97.67±0.40%, respectively, showing an overall accuracy of 93.50±0.42%. Results justify the importance of pixel stiffness for segmentation of thyroid nodules in elastography images.

Transfer learning for automatic joint segmentation of thyroid and breast lesions from ultrasound images.

It plays a significant role to accurately and automatically segment lesions from ultrasound (US) images in clinical application. Nevertheless, it is extremely challenging because distinct components of heterogeneous lesions are similar to background in US images. In our study, a transfer learning-based method is developed for full-automatic joint segmentation of nodular lesions. Transfer learning is a widely used method to build high performing computer vision models. Our transfer learning model is a novel type of densely connected convolutional network (SDenseNet). Specifically, we pre-train SDenseNet based on ImageNet dataset. Then our SDenseNet is designed as a multi-channel model (denoted Mul-DenseNet) for automatically jointly segmenting lesions. As comparison, our SDenseNet using different transfer learning is applied to segmenting nodules, respectively. In our study, we find that more datasets for pre-training and multiple pre-training do not always work in segmentation of nodules, and the performance of transfer learning depends on a judicious choice of dataset and characteristics of targets. Experimental results illustrate a significant performance of the Mul-DenseNet compared to that of other methods in the study. Specially, for thyroid nodule segmentation, overlap metric (OM), dice ratio (DR), true-positive rate (TPR), false-positive rate (FPR) and modified Hausdorff distance (MHD) are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] mm, respectively; for breast nodule segmentation, OM, DR, TPR, FPR and MHD are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] mm, respectively. The experimental results illustrate our transfer learning models are very effective in segmentation of lesions, which also demonstrate that it is potential of our proposed Mul-DenseNet model in clinical applications. This model can reduce heavy workload of the physicians so that it can avoid misdiagnosis cases due to excessive fatigue. Moreover, it is easy and reproducible to detect lesions without medical expertise.

Thyroid Nodule Segmentation and Classification Using Deep Convolutional Neural Network and Rule-based Classifiers.

Thyroid cancer has a high prevalence all over the world. Accurate thyroid nodule diagnosis can lead to effective treatment and decrease the mortality rate. Ultrasound imaging is a safe, portable, and inexpensive tool for thyroid nodule monitoring. However, the widespread use of ultrasound has also resulted in over-diagnosis and over-treatment of nodules. There is also large variability in the assessment and characterization of nodules. Thyroid nodule classification requires precise delineation of the nodule boundary which is tedious and time- consuming. Automatic segmentation of nodule boundaries is highly desirable, however, it is challenging due to the wide range of nodule appearances, shapes, and sizes. In this study, we propose an end-to-end pipeline for nodule segmentation and classification. A residual dilated UNet (resDUnet) model is proposed for nodule segmentation. The output of resDUnet is fed to two rule-based classifiers to categorize the composition and echogenicity of the segmented nodule. We evaluate our segmentation method on a large dataset of 352 ultrasound images reviewed by a certified radiologist. When compared with ground-truth, resDUnet gives a higher Dice score than the standard UNet (82% vs. 81%). Our method requires minimal user interaction and it is robust to reasonable variations in the user-specified region-of-interest. We expect the proposed method to reduce variability in thyroid nodule assessment which results in more efficient and cost-effective monitoring of thyroid cancer.

Image Segmentation of Thyroid Nodule and Capsule for Diagnosing Central Compartment Lymph Node Metastasis.

Thyroid ultrasound (US) image segmentation is of great significance for both doctors and patients. However, it is a challenging task because of the low image quality, low contrast and complex background in each US image. In recent years, some researchers have done thyroid nodule segmentation tasks, but the results achieved are not particularly satisfactory. In this paper, we have broadened the targets of interest and included both thyroid nodules and capsules into our research scope. We propose a method that implements a C-MMDetection to detect and extract the region of interest (ROI), and a modified salient object detection network U2-RNet to segment nodules and capsules respectively. Experiments show that our method segments nodules and capsules in US images more effectively than other networks, which is very helpful for doctors to diagnose central compartment lymph node metastasis (CLNM).

Integrating User-Input into Deep Convolutional Neural Networks for Thyroid Nodule Segmentation.

Delineation of thyroid nodule boundaries is necessary for cancer risk assessment and accurate categorization of nodules. Clinicians often use manual or bounding-box approach for nodule assessment which leads to subjective results. Consequently, agreement in thyroid nodule categorization is poor even among experts. Computer-aided diagnosis systems could reduce this variability by minimizing the extent of user interaction and by providing precise nodule segmentations. In this study, we present a novel approach for effective thyroid nodule segmentation and tracking using a single user click on the region of interest. When a user clicks on an ultrasound sweep, our proposed model can predict nodule segmentation over the entire sequence of frames. Quantitative evaluations show that the proposed method out-performs the bounding box approach in terms of the dice score on a large dataset of 372 ultrasound images. The proposed approach saves expert time and reduces the potential variability in thyroid nodule assessment. The proposed one-click approach can save clinicians time required for annotating thyroid nodules within ultrasound images/sweeps. With minimal user interaction we would be able to identify the nodule boundary which can further be used for volumetric measurement and characterization of the nodule. This approach can also be extended for fast labeling of large thyroid imaging datasets suitable for training machine-learning based algorithms.

Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability

Automatic detection of thyroid nodules with ultrasound images: Basing on semi-supervised learning

Ultrasonography is one of the most common method for the diagnosis of thyroid nodules clinically. But the lack of experienced radiologists has become the biggest problem which reduce the accuracy and effectiveness of thyroid nodules’ detection. What’s more, although artificial intelligence (AI) has been widely used to solve the problem, the performance is limited by the size of the database. In this paper, an improved U-Net is designed for the segmentation of thyroid nodules, following a fully convolution network for the classification of thyroid nodules. During the training, pseudo-label is used for semi-supervised learning. The whole work is trained in 2032 ultrasound images (1000 nodules) and tested in 400 ultrasound images (200 nodules). The average dice coefficient is 89.7% in the test set for segmentation, while the precision and recall is 93.2% and 96% separately for classification. These results are higher than the method which only use the images with true labels, proving the effectiveness of pseudo-label.

Thyroid nodules segmentation methods in clinical ultrasound images: A review

Thyroid is an extremely essential butterfly shaped organ which is positioned in front of neck. For finding thyroid disease, ultrasound imaging is considered a best for identification of thyroid nodules because of its inexpensive and trouble-free nature. For diagnosis of thyroid disorder, it is very important to separate thyroid nodules from thyroid gland. For the same in biomedical image processing, image segmentation and classification followed by image preprocessing is very imperative steps. The key goal of this work is to categorized of thyroid nodules, thyroid gland segmentation methods. This paper explained different segmentation methods like counter and shaped based, region based and machine learning and deep learning strategies for thyroid gland and thyroid nodules. In addition, grouping of thyroid knobs is classified by traditional and convolution neural network for different feature extraction methods. All these methods are evaluated and compared by different quality metrics. After careful assessment of plenty of paper we did comparative and comprehensive analysis of segmentation methods with deep learning. Segmentation by deep learning approach gives better performance but it required more marked data base. In thyroid nodules segmentation region-based methods and contour and shape-based methods gives better performance. This paper concludes segmentation methods and their performance which is based on image attributes applications etc. The performance is assets by accuracy, selectivity and specificity and mean overlap parameters. Moreover, we concentrate on several advantages and disadvantages of current thyroid nodules ultrasound image segmentation of thyroid nodules and thyroid gland methods for 2Dimentionsl Ultrasound image, which would be useful for future studies such as identification of specific thyroid disease.

Ultrasound Image Segmentation of Thyroid Nodules Based On Joint Up-sampling

In view of the problem of inaccurate segmentation of thyroid nodule ultrasound images, a method of thyroid nodule ultrasound image segmentation based on joint up-sampling is proposed. In order to solve the problem of serious speckle noise in the original ultrasound data, preprocessing is performed to enhance the contrast between the nodule area and the background area. In order to achieve accurate positioning of the nodule target area, a joint up-sampling module is designed to fuse the context information of ordinary standard convolution and expansion convolution with different expansion coefficients. Experimental results show that the proposed method achieves 93.19% accuracy and dice similarity coefficient is 0.8558, which is better than other existing thyroid nodule segmentation network models.