Dual-stream Assisted U-Net for Thyroid Nodule Segmentation
Authors: 
Qi Zhou, Hang Yang, chuangeng Tian, Yu Hui, Lu TangAuthors Info & Claims
Qi Zhou, Hang Yang, chuangeng Tian, Yu Hui, Lu TangAuthors Info & ClaimsArticle No.: 16, Pages 1 - 5
Abstract
Abstract—Accurate and effective segmentation of thyroid nodules in ultrasound images is critical for computer-aided nodule diagnosis and treatment. However, due to the ill-defined margin of ultrasound images and the complexity of thyroid tissue structure, accurate segmentation of fine contours is still challenging. We propose a dual-stream network-assisted U-Net (DA-UNet) for these issues, which fully integrates features extracted by convolutional neural networks and vision transformer (ViT), applying them to the decoder and skip connection stages. The model is evaluated on the publicly available dataset TN3K, and the experimental results show that DA-UNet achieves 84.43% and 85.55% in Dice coefficient and F1-score respectively, with an average improvement of 3.75% and 3.12% compared to the current popular segmentation networks, reaching the optimal level.
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- Dual-stream Assisted U-Net for Thyroid Nodule Segmentation
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Published In
September 2023
133 pages
ISBN:9798400708312
DOI:10.1145/3625343
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 November 2023
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- Research-article
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- Refereed limited
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- Xu Zhou Science and Technology Program
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AIMLR 2023
AIMLR 2023: 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics
September 15 - 17, 2023
Bangkok, Thailand
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