{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:45Z","timestamp":1784217885854,"version":"3.55.0"},"reference-count":37,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12475343"],"award-info":[{"award-number":["12475343"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Medical image segmentation is fundamental to radiotherapy planning, yet accurate delineation of organs at risk and tumor targets remain challenging due to anatomical variability and low soft\u2010tissue contrast in CT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a lightweight, high\u2010precision automatic segmentation network that meets the dual clinical requirements of accuracy and computational efficiency for online adaptive liver cancer radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We propose FS\u2010Mamba, a U\u2010shaped encoder\n                      <jats:sup>21<\/jats:sup>\n                      \u2010decoder network built upon a novel Frequency\u2010Long SSM Block that integrates three innovations: (1) a flip\u2010selective scanning strategy (FS\u2010Scanning) that dynamically reorders feature sequences to prioritize salient information; (2) a frequency\u2010domain modeling branch using Fast Fourier Transform to enhance boundary discrimination; and (3) a long\u2010memory state space model (LSSM) that augments the A\u2010matrix to strengthen historical state retention. The model was validated on three datasets: the public CT\u2010ORG (140 CT volumes, 6 organs) and Synapse (30 cases, 8 organs) benchmarks, and an in\u2010house liver cancer dataset (50 patients, 12 structures including PTV and GTV). Comparisons were made against UNet, nnUNet, TransUNet, Swin\u2010UMamba, and the clinically mainstream TotalSegmentator using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and subjective physicist scoring.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      FS\u2010Mamba achieved mean DSC\/HD95 of 92.81%\/10.74 on CT\u2010ORG, 81.28%\/14.32 on Synapse\n                      <jats:sup>30<\/jats:sup>\n                      , and 92.68%\/10.17 on the liver cancer dataset, outperforming all compared methods with statistical significance (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05). It received the highest mean subjective clinical score of 4.69\/5.0 from 10 blinded medical physicists, approaching the ground\u2010truth reference (4.94\/5.0). Despite its leading accuracy, FS\u2010Mamba maintains the smallest model size (20.57\u00a0M parameters, 31.94 GFLOPs) and fastest inference (20.16\u00a0ms per case), representing a 4.5x parameter reduction and 6.5x speedup compared to TotalSegmentator.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>FS\u2010Mamba provides a clinically viable solution for automated multi\u2010organ and tumor target segmentation that satisfies the stringent time constraints of online adaptive radiotherapy while maintaining expert\u2010level accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70677","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:36:12Z","timestamp":1783485372000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FS\u2010Mamba: Feature\u2010wise scanning Mamba UNet for automatic image segmentation in liver tumor radiotherapy"],"prefix":"10.1002","volume":"27","author":[{"given":"Peijun","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Radiation Physics Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueren","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Science Sun Yat\u2010sen University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Gynecological Radiotherapy Harbin Medical University Cancer Hospital  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zekun","family":"Jiang","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center West China Hospital, Sichuan University  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingtao","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010023\u201001885\u20100"},{"issue":"7","key":"e_1_2_9_3_1","first-page":"3523","article-title":"Image segmentation using deep learning: a survey","volume":"44","author":"Minaee S","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871620"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.12.075"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.04.027"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.04.026"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(20)30034\u20106"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010022\u201034257\u2010x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010022\u201002102\u20106"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"NavarroF SasaharaG ShitS et\u00a0al.A unified 3d framework for organs\u2010at\u2010risk localization and segmentation for radiation therapy planning. 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