Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation
Thyroid nodules are detected in up to 65% of the general population, and their early identification is clinically significant for formulating personalized treatment plans. Precise segmentation of nodules in ultrasound images is essential for assessing their morphological and pathological characteris...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11121866/ |
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| author | Zhiheng Zhang Lin Li Cheng Zhao Peng Ren Ran Zhang |
| author_facet | Zhiheng Zhang Lin Li Cheng Zhao Peng Ren Ran Zhang |
| author_sort | Zhiheng Zhang |
| collection | DOAJ |
| description | Thyroid nodules are detected in up to 65% of the general population, and their early identification is clinically significant for formulating personalized treatment plans. Precise segmentation of nodules in ultrasound images is essential for assessing their morphological and pathological characteristics. In current clinical practice, the annotation of nodule regions primarily relies on manual operations performed by physicians. This process is not only time-consuming but also labor-intensive. Furthermore, due to variations in physicians’ professional experience, the annotation results are difficult to ensure consistency and reliability. This study proposes an asymmetric encoder-decoder segmentation architecture based on Convolutional Neural Networks (CNN) and attention mechanisms for automatically and precisely segmenting thyroid nodules in ultrasound images. The framework introduces an Efficient Convolutional Block (ECB) to extract high-level semantic features, constructs a Convolutional Modulation Module (CMM) to enhance feature representation, and incorporates a Spatial Semantic Enhancement Module (SSEM) to optimize detail reconstruction. Experimental results on a public thyroid nodule ultrasound dataset demonstrate that the proposed method significantly outperforms existing models’ overall segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 0.8332 and an Intersection over Union (IOU) of 0.7473 on the test set. Ablation studies and visual analysis further confirm the effectiveness and interpretability of each module. |
| format | Article |
| id | doaj-art-da3118599408445ea2baa669387c92fb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-da3118599408445ea2baa669387c92fb2025-08-20T03:07:10ZengIEEEIEEE Access2169-35362025-01-011314190814191910.1109/ACCESS.2025.359746011121866Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule SegmentationZhiheng Zhang0Lin Li1Cheng Zhao2Peng Ren3https://orcid.org/0000-0001-7883-1873Ran Zhang4https://orcid.org/0000-0001-8628-3803School of Stomatology, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaThyroid nodules are detected in up to 65% of the general population, and their early identification is clinically significant for formulating personalized treatment plans. Precise segmentation of nodules in ultrasound images is essential for assessing their morphological and pathological characteristics. In current clinical practice, the annotation of nodule regions primarily relies on manual operations performed by physicians. This process is not only time-consuming but also labor-intensive. Furthermore, due to variations in physicians’ professional experience, the annotation results are difficult to ensure consistency and reliability. This study proposes an asymmetric encoder-decoder segmentation architecture based on Convolutional Neural Networks (CNN) and attention mechanisms for automatically and precisely segmenting thyroid nodules in ultrasound images. The framework introduces an Efficient Convolutional Block (ECB) to extract high-level semantic features, constructs a Convolutional Modulation Module (CMM) to enhance feature representation, and incorporates a Spatial Semantic Enhancement Module (SSEM) to optimize detail reconstruction. Experimental results on a public thyroid nodule ultrasound dataset demonstrate that the proposed method significantly outperforms existing models’ overall segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 0.8332 and an Intersection over Union (IOU) of 0.7473 on the test set. Ablation studies and visual analysis further confirm the effectiveness and interpretability of each module.https://ieeexplore.ieee.org/document/11121866/Attention mechanismconvolutional neural networkdeep learningmedical image computingthyroid nodule segmentation |
| spellingShingle | Zhiheng Zhang Lin Li Cheng Zhao Peng Ren Ran Zhang Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation IEEE Access Attention mechanism convolutional neural network deep learning medical image computing thyroid nodule segmentation |
| title | Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation |
| title_full | Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation |
| title_fullStr | Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation |
| title_full_unstemmed | Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation |
| title_short | Asymmetric Network Based on CNN and Attention Mechanisms for Thyroid Nodule Segmentation |
| title_sort | asymmetric network based on cnn and attention mechanisms for thyroid nodule segmentation |
| topic | Attention mechanism convolutional neural network deep learning medical image computing thyroid nodule segmentation |
| url | https://ieeexplore.ieee.org/document/11121866/ |
| work_keys_str_mv | AT zhihengzhang asymmetricnetworkbasedoncnnandattentionmechanismsforthyroidnodulesegmentation AT linli asymmetricnetworkbasedoncnnandattentionmechanismsforthyroidnodulesegmentation AT chengzhao asymmetricnetworkbasedoncnnandattentionmechanismsforthyroidnodulesegmentation AT pengren asymmetricnetworkbasedoncnnandattentionmechanismsforthyroidnodulesegmentation AT ranzhang asymmetricnetworkbasedoncnnandattentionmechanismsforthyroidnodulesegmentation |