The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The i...
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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/11020702/ |
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| author | Jingguo Qu Xinyang Han Man-Lik Chui Yao Pu Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying |
| author_facet | Jingguo Qu Xinyang Han Man-Lik Chui Yao Pu Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying |
| author_sort | Jingguo Qu |
| collection | DOAJ |
| description | Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies. |
| format | Article |
| id | doaj-art-37ea7a5a56d44ce98df61eb7bb9ce3bb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-37ea7a5a56d44ce98df61eb7bb9ce3bb2025-08-20T03:24:59ZengIEEEIEEE Access2169-35362025-01-0113972089722710.1109/ACCESS.2025.357545411020702The Application of Deep Learning for Lymph Node Segmentation: A Systematic ReviewJingguo Qu0https://orcid.org/0009-0006-4265-3714Xinyang Han1Man-Lik Chui2Yao Pu3https://orcid.org/0009-0004-3133-5054Simon Takadiyi Gunda4Ziman Chen5https://orcid.org/0009-0009-9166-143XJing Qin6https://orcid.org/0000-0002-2961-0860Ann Dorothy King7Winnie Chiu-Wing Chu8https://orcid.org/0000-0003-4962-4132Jing Cai9https://orcid.org/0000-0001-6934-0108Michael Tin-Cheung Ying10https://orcid.org/0000-0001-5979-6072Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaCentre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaAutomatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.https://ieeexplore.ieee.org/document/11020702/Convolutional neural networkdeep learninglymph node segmentationmedical image processingtransformer |
| spellingShingle | Jingguo Qu Xinyang Han Man-Lik Chui Yao Pu Simon Takadiyi Gunda Ziman Chen Jing Qin Ann Dorothy King Winnie Chiu-Wing Chu Jing Cai Michael Tin-Cheung Ying The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review IEEE Access Convolutional neural network deep learning lymph node segmentation medical image processing transformer |
| title | The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review |
| title_full | The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review |
| title_fullStr | The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review |
| title_full_unstemmed | The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review |
| title_short | The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review |
| title_sort | application of deep learning for lymph node segmentation a systematic review |
| topic | Convolutional neural network deep learning lymph node segmentation medical image processing transformer |
| url | https://ieeexplore.ieee.org/document/11020702/ |
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