{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:02:11Z","timestamp":1776834131798,"version":"3.51.2"},"reference-count":43,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate lymph node (LN) segmentation is highly beneficial for diagnosing and treating head and neck diseases. However, because of the varying sizes and complex shapes of LNs from the head and neck, as well as their blurred boundaries with surrounding tissues in computed tomography (CT) images, it is difficult for physicians to manually identify the region of interest (ROI). Although existing 3D\u2010volumetric\u2010convolution\u2010based methods play an important role in LN boundary extraction, they suffer from high computational complexity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To tackle these issues, we develop an efficient and lightweight volumetric convolutional neural network, named LNSNet, for the LN segmentation from the head and neck region.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our LNSNet presented a 3D Volume Block, which mainly combines Volumetric Partial Convolution (VPConv) with point\u2010wise convolution to decrease computational complexity and parameter count. In addition, both a Lightweight Boundary Enhancement Module (LBEM) and a depthwise separable convolution are added to the bottom of LNSNet to improve the accuracy of LN segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>678 3D LNs extracted from 123 patients with head and neck cancer were used for evaluation. We trained the model using 5\u2010fold cross\u2010validation and tested it on an independent test set. Our model had fewer parameters and lower computational complexity than some state\u2010of\u2010the\u2010art models, with a Dice Similarity Coefficient (DSC) of up to 73.81% and the Average Surface Distance (ASD) and 95th percentile\u2018 Hausdorff Distance (HD95) are only 0.92 and 2.52\u00a0mm, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LNSNet improves computational efficiency and robustness by reducing parameter count and complexity, making it more attractive in practical\u00a0applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70123","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T06:51:21Z","timestamp":1762239081000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight deep training network for lymph nodes segmentation from head and neck CT images"],"prefix":"10.1002","volume":"52","author":[{"given":"Fan","family":"Lu","sequence":"first","affiliation":[{"name":"School of Future Science and Engineering Soochow University Suzhou Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao\u2010Long","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital Fudan University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binbin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key laboratory of Carcinogenesis and Translational Research (Ministry of Education\/Beijing), department of Ultrasonography Peking University Cancer Hospital &amp; Institute Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key laboratory of Carcinogenesis and Translational Research (Ministry of Education\/Beijing), department of Ultrasonography Peking University Cancer Hospital &amp; Institute Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caiyin","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiology Taizhou People's Hospital Affiliated to Nanjing Medical University Taizhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Li","sequence":"additional","affiliation":[{"name":"Center of Stomatology the Second Affiliated Hospital of Soochow University Suzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Future Science and Engineering Soochow University Suzhou Jiangsu China"},{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220329"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102312"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109870"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2025.102535"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16142538"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20210515"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106752"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106519"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108890"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2024.106796"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109925"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation. 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