{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:31:44Z","timestamp":1753860704467,"version":"3.41.2"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole\u2010body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinical\u00a0decision\u2010making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre\u2010 and post\u2010diagnosis CT scans were used to validate the segmentation and registration process. A two\u2010phase bone\u2010wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non\u2010rigid registration method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric\u2010wise, the nonrigid method performed better but diminished lesion visibility in difference images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axial\u00a0spine.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70053","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T05:28:31Z","timestamp":1741757311000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bone\u2010wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5266-8502","authenticated-orcid":false,"given":"Arttu","family":"Ruohola","sequence":"first","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ville","family":"Haapam\u00e4ki","sequence":"additional","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8797-6094","authenticated-orcid":false,"given":"Eero","family":"Salli","sequence":"additional","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuomas","family":"Kaseva","sequence":"additional","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marko","family":"Kangasniemi","sequence":"additional","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sauli","family":"Savolainen","sequence":"additional","affiliation":[{"name":"HUS Diagnostic Center Department of Radiology University of Helsinki and Helsinki University Hospital Helsinki Finland"},{"name":"Department of Physics University of Helsinki Helsinki Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(14)70442-5"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0009-9260(67)80010-2"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.12007"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/medicina56040195"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11914-023-00795-6"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97607-7"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims-20-1367"},{"key":"e_1_2_9_9_1","first-page":"3173","article-title":"Non\u2010rigid image registration for temporal subtraction of whole\u2010body nuclear emission images","volume":"5","author":"Claessens R","year":"2003","journal-title":"2003 IEEE Nucl Sci Symp."},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017161942"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/tomography9020062"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.07.024"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06631-9"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20190364"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12178542"},{"key":"e_1_2_9_16_1","article-title":"Temporal subtraction method for lung nodule detection on successive thoracic CT soft\u2010copy images","volume":"271","author":"Aoki T","year":"2013","journal-title":"Radiology"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-020-05449-2"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.11889"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2016.12.003"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"KrcahM Sz\u00e9kelyG BlancR.Fully automatic and fast segmentation of the femur bone from 3D\u2010CT images with no shape prior.Paper Presented at: IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2011; Chicago IL.IEEE;2011:2087\u20102090.","DOI":"10.1109\/ISBI.2011.5872823"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7657-3_19"},{"key":"e_1_2_9_22_1","doi-asserted-by":"crossref","unstructured":"MilletariF NavabN AhmadiS.V\u2010Net: Fully convolutional neural networks for volumetric medical image segmentation.Paper presented at: 2016 Fourth International Conference on 3D Vision (3DV); Los Alamitos CA.IEEE Computer Society;2016;565\u2010571.","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_2_9_23_1","unstructured":"The MONAI Consortium Project MONAI 2020."},{"key":"e_1_2_9_24_1","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke A","year":"2019"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v086.i08"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0165-9"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2013.00045"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(91)90025-E"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.54294\/uvnhin"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87564-6"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-59440-6"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.2307\/1932409"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.03.010"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-022-02088-1"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33454-2_18"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdi235"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470-7330.2004.0010"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/bcj.2015.57"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2015.02.013"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2014.57.9961"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1002\/hon.2586"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:29:15Z","timestamp":1746685755000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1002\/acm2.70053"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70053","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"type":"print","value":"1526-9914"},{"type":"electronic","value":"1526-9914"}],"subject":[],"published":{"date-parts":[[2025,3,11]]},"assertion":[{"value":"2024-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70053"}}