TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision. (March 2022)

• Record Type: Journal Article
• Authors: Sun, Jiawei; Li, Chunying; Lu, Zhengda; He, Mu; Zhao, Tong; Li, Xiaoqin; Gao, Liugang; Xie, Kai; Lin, Tao; Sui, Jianfeng; Xi, Qianyi; Zhang, Fan; Ni, Xinye
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Thyroid nodules -- Ultrasound imaging -- Segmentation -- Soft shape supervision -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106600
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Propose a dual-path network to fuse both region and shape information for automatic thyroid nodule segmentation. Use a novel cross-path attention mechanism to implement soft shape supervision on segmentation. Evaluate TNSNet by comparative analysis. Abstract: 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.