{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:19:59Z","timestamp":1716941999625},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Automated segmentation of lung tumors attached to anatomic structures such as the chest wall or mediastinum remains a technical challenge because of the similar Hounsfield units of these structures. To address this challenge, we propose herein a spline curve deformation model that combines prior shapes to correct large spatially contiguous errors (LSCEs) in input shapes derived from image\u2010appearance cues.The model is then used to identify the adhesion boundaries between large lung tumors and tissue around the lungs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The deformation of the whole curve is driven by the transformation of the control points (CPs) of the spline curve, which are influenced by external and internal forces. The external force drives the model to fit the positions of the non\u2010LSCEs of the input shapes while the internal force ensures the local similarity of the displacements of the neighboring CPs. The proposed model corrects the gross errors in the lung input shape caused by large lung tumors, where the initial lung shape for the model is inferred from the training shapes by shape group\u2010based sparse prior information and the input lung shape is inferred by adaptive\u2010thresholding\u2010based segmentation followed by morphological refinement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The accuracy of the proposed model is verified by applying it to images of lungs with either moderate large\u2010sized (ML) tumors or giant large\u2010sized (GL) tumors. The quantitative results in terms of the averages of the dice similarity coefficient (DSC) and the Jaccard similarity index (SI) are 0.982\u00a0\u00b1\u00a00.006 and 0.965\u00a0\u00b1\u00a00.012 for segmentation of lungs adhered by ML tumors, and 0.952\u00a0\u00b1\u00a00.048 and 0.926\u00a0\u00b1\u00a00.059 for segmentation of lungs adhered by GL tumors, which give 0.943\u00a0\u00b1\u00a00.021 and 0.897\u00a0\u00b1\u00a00.041 for segmentation of the ML tumors, and 0.907\u00a0\u00b1\u00a00.057 and 0.888\u00a0\u00b1\u00a00.091 for segmentation of the GL tumors, respectively. In addition, the bidirectional Hausdorff distances are 5.7\u00a0\u00b1\u00a01.4 and 11.3\u00a0\u00b1\u00a02.5\u00a0mm for segmentation of lungs with ML and GL tumors, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>When combined with prior shapes, the proposed spline curve deformation can deal with large spatially consecutive errors in object shapes obtained from image\u2010appearance information. We verified this method by applying it to the segmentation of lungs with large tumors adhered to the tissue around the lungs and the large tumors. Both the qualitative and quantitative results are more accurate and repeatable than results obtained with current state\u2010of\u2010the\u2010art techniques.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13998","type":"journal-article","created":{"date-parts":[[2019,12,28]],"date-time":"2019-12-28T20:00:25Z","timestamp":1577563225000},"page":"1011-1020","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images"],"prefix":"10.1002","volume":"47","author":[{"given":"Xin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering Hebei University Hebei Baoding 071000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering Hebei University Hebei Baoding 071000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[{"name":"Hebei University Affiliated Hospital Hebei Baoding 071000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Information Science Hebei University Hebei Baoding 071000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixu","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200000 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.03.008"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.04.001"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.07.031"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4793409"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"NianY LiMCui H et al.Graph\u2010based unsupervised segmentation for lung tumor CT images.Proceedings of the IEEE International Conference on Computer and Communications;2017:1884\u20131888.","DOI":"10.1109\/CompComm.2017.8322865"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2012.12.004"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.10.005"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer\u2010Assisted Intervention \u2013 MICCAI;2015 vol 9351:234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_8_11_1","doi-asserted-by":"crossref","unstructured":"ZhouZ SiddiqueeMMR TajbakhshN LiangJ.UNet++: A Nested U\u2010Net Architecturefor Medical Image Segmentation. 4th Deep Learning in Medical Image Analysis (DLMIA 2018\/ML\u2010CDS 2018) Workshop LNCS 11045:3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1656593"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3222872"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3003066"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2012.08.002"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.10.005"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.07.003"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2606370"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4929562"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-015-1150-0"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.03.002"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4907961"},{"key":"e_1_2_8_23_1","first-page":"479154","article-title":"Robust Initialization of active shape models for lung segmentation in CT scans: a feature\u2010based atlas approach","volume":"13","author":"Gill G","year":"2014","journal-title":"Int J Biomed Imaging"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1995.1004"},{"key":"e_1_2_8_25_1","doi-asserted-by":"crossref","unstructured":"NahedJA JollyMP YangGZ.Robust active shape models: a robust generic and simple automatic segmentation tool.Proceedings of the International Conference on Medical Image Computing and Computer\u2010Assisted Intervention;2006:1\u20108.","DOI":"10.1007\/11866763_1"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2006.889726"},{"key":"e_1_2_8_27_1","doi-asserted-by":"crossref","unstructured":"ZhangS ZhanY DewanM HuangJ MetaxasDN ZhouXS.Sparse shape composition: a new framework for shape prior modeling.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition;2011:1025\u20131032.","DOI":"10.1109\/CVPR.2011.5995322"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.08.004"},{"key":"e_1_2_8_29_1","doi-asserted-by":"crossref","unstructured":"ZhangS ZhanY ZhouY UzunbasM MetaxasDN.Shape prior modeling using sparse representation and online dictionary learning.Proceedings of the Medical Image Computing and Computer\u2010Assisted Intervention;2012:435\u2013442.","DOI":"10.1007\/978-3-642-33454-2_54"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.09.001"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.06.003"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/83.846249"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/79.799930"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.295"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.233"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fmp.13998","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.13998","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.13998","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.13998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T23:11:51Z","timestamp":1694041911000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.13998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,28]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["10.1002\/mp.13998"],"URL":"https:\/\/doi.org\/10.1002\/mp.13998","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,28]]},"assertion":[{"value":"2019-01-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-02","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-01-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}