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

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 7×1, 1×7 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.

/pdf/local-and-context-attention-adaptive-lca-net-for-thyroid-3s8nu9ty.pdf

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

The quality of segmentation of thyroid nodules in ultrasound images is a crucial factor in preventing the cancerization of thyroid nodules. However, the existing standards for the ultrasound imaging of cancerous nodules have limitations, and changes of the echo pattern of thyroid nodules pose challenges in accurately segmenting nodules, which can affect the diagnostic results of medical professionals.The aim of this study is to address the challenges related to segmentation accuracy due to noise, low contrast, morphological scale variations, and blurred edges of thyroid nodules in ultrasound images and improve the accuracy of ultrasound-based thyroid nodule segmentation, thereby aiding the clinical diagnosis of thyroid nodules.In this study, the dataset of thyroid ultrasound images was obtained from Hunan Provincial People's Hospital, consisting of a total of 3572 samples used for the training, validation, and testing of this model at a ratio of 8:1:1. A novel SK-Unet++ network was used to enhance the segmentation accuracy of thyroid nodules. SK-Unet++ is a novel deep learning architecture that adds the adaptive receptive fields based on the selective kernel (SK) attention mechanisms into the Unet++ network. The convolution blocks of the original UNet++ encoder part were replaced with finer SK convolution blocks in SK-Unet++. First, multiple skip connections were incorporated so that SK-Unet++ can make information from previous layers of the neural network to bypass certain layers and directly propagate to subsequent layers. The feature maps of the corresponding locations were fused on the channel, resulting in enhanced segmentation accuracy. Second, we added the adaptive receptive fields. The adaptive receptive fields were used to capture multiscale spatial features better by dynamically adjusting its receptive field. The assessment metrics contained dice similarity coefficient (Dsc), accuracy (Acc), precision (Pre), recall (Re), and Hausdorff distance, and all comparison experiments used the paired t-tests to assess whether statistically significant performance differences existed (p < 0.05). And to address the multi-comparison problem, we performed the false discovery rate (FDR) correction after the test.The segmentation model had an Acc of 80.6%, Dsc of 84.7%, Pre of 77.5%, Re of 71.7%, and an average Hausdorff distance of 15.80 mm. Ablation experimental results demonstrated that each module in the network could contribute to the improved performance (p < 0.05) and determined the best combination of parameters. A comparison with other state-of-the-art methods showed that SK-Unet++ significantly outperformed them in terms of segmentation performance (p < 0.05), with a more accurate segmentation contour. Additionally, the adaptive weight changes of the SK module were monitored during the training process, and the resulting change curves demonstrated their convergence.Our proposed method demonstrates favorable performance in the segmentation of ultrasound images of thyroid nodules. Results confirmed that SK-Unet++ is a feasible and effective method for the automatic segmentation of thyroid nodules in ultrasound images. The high accuracy achieved by our method can facilitate efficient screening of patients with thyroid nodules, ultimately reducing the workload of clinicians and radiologists. 

SK-Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images.

Boundary-oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

Ultrasound imaging technology plays an important role to assist doctors in diagnosing thyroid nodules. The tissue structure around the thyroid is very complex, which makes it difficult to segment and extract the ultrasound image of thyroid nodules accurately. For address this problem, this paper proposes a model algorithm for thyroid nodule ultrasound image segmentation using ASPP fusion features. First, spatial pyramid pooling and depthwise separable convolution are combined in order to solve the problem that the size of the mapping feature will change in the process of better capturing the context information. Besides, Atrous Spatial Pyramid Pooling (ASPP) is proposed to achieve the purpose of processing input image channel and spatial information separately. In order to appropriately reduce the dimension and size of feature images, a $1\times 1$ convolution operation is performed before each convolution calculation, and the model size is optimized. In the decoding stage, decoder module appropriately adjusts the feature map with a relatively low resolution previously from decoder module, and sets the output channel number of two convolutions to the same value. All features have the same dimension by adjustment, and features can be fused by element-wise summation. Finally, Dice Similarity Coefficient (DSC), Prevent Match (PM) and Correspondence Patio (CR) are used as evaluation criteria to compare with other model algorithms. The experimental results show that the proposed model can significantly improve the segmentation effect of ultrasound images for thyroid nodules compared with traditional models.

/pdf/ultrasound-image-segmentation-method-for-thyroid-nodules-2ignb5j8gx.pdf

Ultrasound Image Segmentation Method for Thyroid Nodules Using ASPP Fusion Features

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Ultrasound Image Segmentation Method for Thyroid Nodules Using ASPP Fusion Features