{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T16:29:33Z","timestamp":1789230573131,"version":"build-2803163510"},"reference-count":51,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Young and Middle-aged Talents Training Program of State Ethnic Affairs Commission","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"crossref","award":["20JR5RA511"],"award-info":[{"award-number":["20JR5RA511"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Fund for the Central Universities","doi-asserted-by":"crossref","award":["31920230172"],"award-info":[{"award-number":["31920230172"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Youth Ph.D. Foundation of Education Department of Gansu Province","award":["2021QB-063"],"award-info":[{"award-number":["2021QB-063"]}]},{"name":"Gansu Province\u2019s Key Provincial Talents Program","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61562075"],"award-info":[{"award-number":["61562075"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Plan of Gansu Province","award":["21YF5GA063"],"award-info":[{"award-number":["21YF5GA063"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,2,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Bone is a common site for the metastasis of malignant tumors, and single photon emission computed tomography (SPECT) is widely used to detect these metastases. Accurate delineation of metastatic bone lesions in SPECT images is essential for developing treatment plans. However, current clinical practices rely on manual delineation by physicians, which is prone to variability and subjective interpretation. While computer-aided diagnosis systems have the potential to improve diagnostic efficiency, fully automated segmentation approaches frequently suffer from high false positive rates, limiting their clinical utility. <jats:italic>Approach.<\/jats:italic> This study proposes an interactive segmentation framework for SPECT images, leveraging the deep convolutional neural networks to enhance segmentation accuracy. The proposed framework incorporates a U-shaped backbone network that effectively addresses inter-patient variability, along with an interactive attention module that enhances feature extraction in densely packed bone regions. <jats:italic>Main results.<\/jats:italic> Extensive experiments using clinical data validate the effectiveness of the proposed framework. Furthermore, a prototype tool was developed based on this framework to assist in the clinical segmentation of metastatic bone lesions and to support the creation of a large-scale dataset for bone metastasis segmentation. <jats:italic>Significance.<\/jats:italic> In this study, we proposed an interactive segmentation framework for metastatic lesions in bone scintigraphy to address the challenging task of labeling low-resolution, large-size SPECT bone scans. The experimental results show that the model can effectively segment the bone metastases of lung cancer interactively. In addition, the prototype tool developed based on the model has certain clinical application value.<\/jats:p>","DOI":"10.1088\/1361-6560\/adaf07","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T22:57:46Z","timestamp":1738018666000},"page":"045009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Interactive segmentation for accurately isolating metastatic lesions from low-resolution, large-size bone scintigrams"],"prefix":"10.1088","volume":"70","author":[{"given":"Xiaoqiang","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3842-2634","authenticated-orcid":true,"given":"Qiang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianwu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchun","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxing","family":"Man","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caihong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"pmbadaf07bib1","first-page":"35","article-title":"Econet: efficient convolutional online likelihood network for scribble-based interactive segmentation","author":"Asad","year":"2022"},{"key":"pmbadaf07bib2","first-page":"564","article-title":"Adaptive multi-scale online likelihood network for AI-assisted interactive segmentation","author":"Asad","year":"2023"},{"key":"pmbadaf07bib3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.mporth.2012.02.002","article-title":"The modern surgical and non-surgical management of appendicular skeletal metastases","volume":"26","author":"Ashford","year":"2012","journal-title":"Orthop. Trauma"},{"key":"pmbadaf07bib4","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbadaf07bib5","first-page":"105","article-title":"Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images","author":"Boykov","year":"2001"},{"key":"pmbadaf07bib6","first-page":"205","article-title":"Swin-unet: unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"pmbadaf07bib7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104068","article-title":"Segmentation of lung cancer-caused metastatic lesions in bone scan images using self-defined model with deep supervision","volume":"79","author":"Cao","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"pmbadaf07bib8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104776","article-title":"CSR-net: cross-scale residual network for multi-objective scaphoid fracture segmentation","volume":"137","author":"Chen","year":"2021a","journal-title":"Comput. Biol. Med."},{"key":"pmbadaf07bib9","doi-asserted-by":"publisher","first-page":"3860","DOI":"10.1002\/mp.14903","article-title":"Learning fuzzy clustering for SPECT\/CT segmentation via convolutional neural networks","volume":"48","author":"Chen","year":"2021b","journal-title":"Med. Phys."},{"key":"pmbadaf07bib10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280","article-title":"Transunet: transformers make strong encoders for medical image segmentation","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"pmbadaf07bib11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21147\/j.issn.1000-9604.2018.01.01","article-title":"Cancer incidence and mortality in China, 2014","volume":"30","author":"Chen","year":"2014","journal-title":"Chin. J. Cancer Res."},{"key":"pmbadaf07bib12","first-page":"1300","article-title":"Focalclick: towards practical interactive image segmentation","author":"Chen","year":"2022"},{"key":"pmbadaf07bib13","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.3390\/diagnostics13193042","article-title":"Bone metastases lesion segmentation on breast cancer bone scan images with negative sample","volume":"13","author":"Chen","year":"2023","journal-title":"Train. Diagn."},{"key":"pmbadaf07bib14","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1053\/ctrv.2000.0210","article-title":"Metastatic bone disease: clinical features, pathophysiology and treatment strategies","volume":"27","author":"Coleman","year":"2001","journal-title":"Cancer Treat Rev."},{"key":"pmbadaf07bib15","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1007\/s11548-020-02223-x","article-title":"Fast interactive medical image segmentation with weakly supervised deep learning method","volume":"15","author":"Girum","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"pmbadaf07bib16","first-page":"540","article-title":"Deep interactive learning: an efficient labeling approach for deep learning-based osteosarcoma treatment response assessment","author":"Ho","year":"2020"},{"key":"pmbadaf07bib17","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3760\/cma.j.cn112151-20200304-00171","article-title":"Unet 3+: a full-scale connected unet for medical image segmentation","author":"Huang","year":"2020"},{"key":"pmbadaf07bib18","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.cpet.2021.06.013","article-title":"Objective task-based evaluation of artificial intelligence-based medical imaging methods: framework, strategies, and role of the physician","volume":"16","author":"Jha","year":"2021","journal-title":"PET Clin."},{"key":"pmbadaf07bib19","first-page":"109","article-title":"Efficient inference in fully connected CRFS with Gaussian edge potentials","author":"Kr\u00e4henb\u00fchl","year":"2011"},{"key":"pmbadaf07bib20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102862","article-title":"HAL-IA: a hybrid active learning framework using interactive annotation for medical image segmentation","volume":"88","author":"Li","year":"2023","journal-title":"Med. Image Anal."},{"key":"pmbadaf07bib21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.15586\/jkcvhl.v8i4.202","article-title":"Evolving patterns of metastasis in renal cell carcinoma: do we need to perform routine bone imaging?","volume":"8","author":"Lin","year":"2021a","journal-title":"J. Kidney Cancer VHL"},{"key":"pmbadaf07bib22","doi-asserted-by":"publisher","first-page":"3300","DOI":"10.1049\/iet-ipr.2019.1690","article-title":"Classifying functional nuclear images with convolutional neural networks: a survey","volume":"14","author":"Lin","year":"2020b","journal-title":"IET Image Process"},{"key":"pmbadaf07bib23","doi-asserted-by":"publisher","first-page":"5782","DOI":"10.1002\/mp.15196","article-title":"Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules","volume":"48","author":"Lin","year":"2021b","journal-title":"Med. Phys."},{"key":"pmbadaf07bib24","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2022.956720","article-title":"Semi-supervised segmentation of metastasis lesions in bone scan images","volume":"9","author":"Lin","year":"2022a","journal-title":"Front. Mol. Biosci."},{"key":"pmbadaf07bib25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243253","article-title":"Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images","volume":"15","author":"Lin","year":"2020a","journal-title":"PLoS One"},{"key":"pmbadaf07bib26","first-page":"2637","article-title":"Focuscut: diving into a focus view in interactive segmentation","author":"Lin","year":"2022b"},{"key":"pmbadaf07bib27","first-page":"13339","article-title":"Interactive image segmentation with first click attention","author":"Lin","year":"2020c"},{"key":"pmbadaf07bib28","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"pmbadaf07bib29","first-page":"8801","article-title":"Semi-supervised medical image segmentation through dual-task consistency","author":"Luo","year":"2021"},{"key":"pmbadaf07bib30","doi-asserted-by":"publisher","first-page":"6695","DOI":"10.1109\/TPAMI.2021.3100536","article-title":"Abdomenct-1k: is abdominal organ segmentation a solved problem?","volume":"44","author":"Ma","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbadaf07bib31","first-page":"504","article-title":"Interactive learning for assisting whole slide image annotation","author":"Menon","year":"2021"},{"key":"pmbadaf07bib32","first-page":"565","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"pmbadaf07bib33","doi-asserted-by":"publisher","first-page":"4736","DOI":"10.1109\/EMBC48229.2022.9871081","article-title":"Deep learning approaches for bone and bone lesion segmentation on 18FDG PET\/CT imaging in the context of metastatic breast cancer","author":"Moreau","year":"2022"},{"key":"pmbadaf07bib34","first-page":"274","author":"Nickisch","year":"2010"},{"key":"pmbadaf07bib35","article-title":"Attention u-net: learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"pmbadaf07bib36","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/S0950-351X(95)80867-1","article-title":"Thyroid-associated ophthalmopathy: pathogenesis and clinical management","volume":"9","author":"Perros","year":"1995","journal-title":"Baillieres Clin. End. Metab."},{"key":"pmbadaf07bib37","first-page":"457","article-title":"User-guided domain adaptation for rapid annotation from user interactions: a study on pathological liver segmentation","author":"Raju","year":"2020"},{"key":"pmbadaf07bib38","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbadaf07bib39","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1145\/1015706.1015720","article-title":"GrabCut: interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"pmbadaf07bib40","first-page":"720","article-title":"Real-time automatic fetal brain extraction in fetal MRI by deep learning","author":"Salehi","year":"2018"},{"key":"pmbadaf07bib41","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1038\/s41569-021-00527-2","article-title":"Applications of artificial intelligence in cardiovascular imaging","volume":"18","author":"Sermesant","year":"2021","journal-title":"Nat. Rev. Cardiol."},{"key":"pmbadaf07bib42","first-page":"344","article-title":"Investigating convolution-attention model for bone scan image segmentation","author":"Sitaba","year":"2023"},{"key":"pmbadaf07bib43","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/JBHI.2023.3247861","article-title":"Few-shot class-incremental learning for medical time series classification","volume":"28","author":"Sun","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"pmbadaf07bib44","first-page":"419","article-title":"Whole-body bone scan segmentation using SegFormer","author":"Syam","year":"2023"},{"key":"pmbadaf07bib45","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.lungcan.2007.03.013","article-title":"Skeletal metastases in non-small cell lung cancer: a retrospective study","volume":"57","author":"Tsuya","year":"2007","journal-title":"Lung Cancer"},{"key":"pmbadaf07bib46","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1111\/iju.13170","article-title":"Skeletal complications in cancer patients with bone metastases","volume":"23","author":"Tsuzuki","year":"2016","journal-title":"Int. J. Urol."},{"key":"pmbadaf07bib47","first-page":"373","article-title":"Deep interactive object selection","author":"Xu","year":"2016"},{"key":"pmbadaf07bib48","first-page":"605","article-title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation","author":"Yu","year":"2019"},{"key":"pmbadaf07bib49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121282","article-title":"Interactive medical image annotation using improved Attention U-net with compound geodesic distance","volume":"237","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"pmbadaf07bib50","first-page":"548","article-title":"Dual-task mutual learning for semisupervised medical image segmentation","author":"Zhang","year":"2021"},{"key":"pmbadaf07bib51","first-page":"3","article-title":"Unet++: a nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T10:33:45Z","timestamp":1738838025000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adaf07"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2,6]]},"published-print":{"date-parts":[[2025,2,16]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/adaf07","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,6]]},"assertion":[{"value":"Interactive segmentation for accurately isolating metastatic lesions from low-resolution, large-size bone scintigrams","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-09-29","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-27","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-02-06","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}