{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:28:16Z","timestamp":1786991296654,"version":"build-2736575974"},"reference-count":33,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":35,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["EP\/W020408\/1"],"award-info":[{"award-number":["EP\/W020408\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["RS718"],"award-info":[{"award-number":["RS718"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Systems Biology"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The automated segmentation of thyroid nodules from ultrasound images holds significant value in clinical diagnosis and treatment. However, achieving precise segmentation remains a substantial challenge due to issues such as blurred nodule boundaries, variable scales, image noise, and inaccurate annotations. To address these difficulties, this paper proposes a novel medical image segmentation network named MFS\u2010Unet. The network introduces three innovative modules to enhance segmentation performance. First, we designed the multi\u2010path vision mamba (MPV) module, which leverages the advantages of state space models (SSMs) to efficiently capture global contextual information and multi\u2010scale features with linear computational complexity, effectively addressing the problem of significant variations in nodule size. Second, a feature gating (FG) module is deployed in the skip connections between the encoder and decoder. Through an attention mechanism, it dynamically screens and enhances features transmitted from the encoder, suppressing background noise and reinforcing key boundary information of the nodules. Finally, we propose a supervised label rectification (SLR) module, aimed at proactively handling the prevalent issue of label noise in training data. By dynamically adjusting loss weights during training, it guides the model to learn more robust feature representations. We conducted extensive experiments on three public thyroid ultrasound datasets: DDTI, TG3K, and TN3K. The results demonstrate that MFS\u2010Unet achieves superior performance across all evaluation metrics compared with various state\u2010of\u2010the\u2010art segmentation methods, proving its effectiveness and significant potential for precise thyroid nodule segmentation in complex ultrasound environments.<\/jats:p>","DOI":"10.1049\/syb2.70044","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:50:47Z","timestamp":1770295847000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MFS\u2010Unet: A Multi\u2010Path Vision Mamba Network for Precise Thyroid Nodule Segmentation"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7539-5970","authenticated-orcid":false,"given":"Shaoqiang","family":"Wang","sequence":"first","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3180-3370","authenticated-orcid":false,"given":"Zhongran","family":"Liu","sequence":"additional","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiling","family":"Shi","sequence":"additional","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengye","family":"Li","sequence":"additional","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiyao","family":"Liu","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China)  Qingdao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Medical Informational Engineering Shandong University of Traditional Chinese Medicine  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University People's Hospital Qingdao Hospital  Qingdao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Swansea University  Swansea Wales UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41421\u2010022\u201000442\u2010x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0007114524002253"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3313977"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/su15075930"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3152247"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3618215"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4622241"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1573-4412(05)80019-4"},{"key":"e_1_2_9_11_1","unstructured":"M.Heidari S. G.Kolahi andS.Karimijafarbigloo et\u00a0al. \u201cComputation\u2010Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis \u201d preprint arXiv:2406.03430 (2024) https:\/\/doi.org\/10.48550\/arXiv.2406.03430."},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2020.10.259"},{"key":"e_1_2_9_13_1","unstructured":"L.ZhuandB.Liao Q.Zhang X.Wang W.Liu andX.Wang \u201cVision Mamba: Efficient Visual Representation Learning With Bidirectional State Space Model \u201d preprint arXiv:2401.09417 (2024)."},{"key":"e_1_2_9_14_1","unstructured":"A.GuandT.Dao \u201cMamba: Linear\u2010Time Sequence Modeling With Selective State Spaces \u201d preprint arXiv:2312.00752 (2023)."},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3304518"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110812"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88210-5_25"},{"key":"e_1_2_9_18_1","unstructured":"C.Qian K.Han J.Ding et\u00a0al. \u201cAdaptive Label Correction for Robust Medical Image Segmentation With Noisy Labels \u201d preprint arXiv:2503.12218 (2025) https:\/\/doi.org\/10.48550\/arXiv.2503.12218."},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.070"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.2174\/1574893617666220324155813"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245982"},{"key":"e_1_2_9_22_1","first-page":"188","volume-title":"10th International Symposium on Medical Information Processing and Analysis","author":"Pedraza L.","year":"2015"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106389"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.01703"},{"key":"e_1_2_9_25_1","first-page":"1286","volume-title":"IEEE Winter Conference on Applications of Computer Vision","author":"Smith L. 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