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As a fundamental step in computer\u2010aided diagnosis systems, automated MLNs segmentation remains challenging due to three key issues: the small size, blurred margins and large localization variations among patients, which often lead to over\u2010 and under\u2010segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address these challenges, the Small\u2010 and Large\u2010scale Information Collaborative Unet (SLSIC\u2010Unet) has been proposed. This network is specifically designed to aggregate small\u2010scale information, modality\u2010specific features (MSFs) and the spatial relationships across MRI slices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>The proposed SLSIC\u2010Unet incorporates three dedicated strategies corresponding to the aforementioned challenges. First, to prevent small MLNs from being obscured by background information, a small\u2010 and large\u2010scale information collaborative framework (SLSICF) is proposed. This framework utilizes dual interactive pathways: magnified image patches are processed to aggregate small\u2010scale information, preventing the loss of small\u2010area MLN representations, while raw images are simultaneously analyzed to extract large\u2010scale contextual information, ensuring performance for general MLNs. Second, to delineate the fuzzy margins of MLNs, the SLSIC\u2010Unet leverages complementary information across modalities by extracting both modality\u2010specific features (MSFs) and multi\u2010modality\u2010fusion features. Finally, the spatial feature context module (SFCM) is designed to aggregate the spatial relationships of MRI slices, utilizing the spatial distribution tendency of MLNs which mostly exhibit proximity to blood vessels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Evaluated on a cohort of 663 NPC cases, the proposed model achieved valuable segmentation performance, with mean values of 0.801\u00a0\u00b1\u00a00.098 for normalized surface distance (NSD), 0.823\u00a0\u00b1\u00a00.082 for dice similarity coefficient (DSC), and 0.706\u00a0\u00b1\u00a00.107 for intersection over union (IoU) on the test set. Comparative analysis revealed that the SLSIC\u2010Unet significantly outperformed (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) existing state\u2010of\u2010the\u2010art methods in MLNs segmentation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The SLSIC\u2010Unet is capable of accurately segmenting MLNs, and can provide technical support for assisting in Nstaging of nasopharyngeal carcinoma.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70284","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:41:16Z","timestamp":1768531276000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Small and large\u2010scale information collaborative unet for metastatic lymph node segmentation of multi\u2010modal nasopharyngeal carcinoma MRI images"],"prefix":"10.1002","volume":"53","author":[{"given":"Haoyang","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"},{"name":"School of Electronic Information and Automation Guilin University of Aerospace Technology  Guilin Guangxi China"},{"name":"Guangxi Colleges and Universities Key Laboratory of Intelligent Detection and Information Processing  Guilin Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuchao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meilan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"},{"name":"Guangxi Human Physiological Information NonInvasive Detection Engineering Technology Research Center  Guilin Guangxi China"},{"name":"Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments  Guilin Guangxi China"},{"name":"Guangxi Key Laboratory of Metabolic Reprogramming and Intelligent Medical Engineering for Chronic Diseases  Guilin Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"TatanunC RitthipravatP BhongmakapatT TuntiyatornL Automatic segmentation of nasopharyngeal carcinoma from CT images: region growing based technique. 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