{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T09:14:03Z","timestamp":1790932443120,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["2020\u20100\u201001524"],"award-info":[{"award-number":["2020\u20100\u201001524"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Multi\u2010organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector\u2010based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation\u00a0performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We propose UNet\u2010based multi\u2010organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information\u00a0effectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed method consists of the following steps: Noise reduction in bin\u2010wise images, image\u2010based material decomposition, generating VMIs, and deep learning\u2010based segmentation. VMIs are synthesized for various x\u2010ray energies using basis images. The UNet\u2010based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual\u2010 and quad\u2010energy bins. We compared it to the conventional PCCT\u2010based segmentation, which uses only the (noise\u2010reduced) bin\u2010wise images. The experiments were conducted on two cases by adjusting the dose\u00a0levels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin\u2010wise images from dual\u2010energy bins; 3D UNet).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed method using VMIs improves training stability for multi\u2010organ segmentation in PCCT, particularly when the number of energy bins is\u00a0small.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17440","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T12:08:31Z","timestamp":1728302911000},"page":"481-488","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["UNet\u2010based multi\u2010organ segmentation in photon counting CT using virtual monoenergetic images"],"prefix":"10.1002","volume":"52","author":[{"given":"Sumin","family":"Baek","sequence":"first","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Hye","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science College of Arts &amp; Science Georgia State University Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okkyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3609097"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2769104"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4922654"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4794178"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultramic.2015.05.002"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4955438"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12863"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: Convolutional networks for biomedical image segmentation. In:NavabN HorneggerJ WellsW FrangiAeds.Medical Image Computing and Computer\u2010Assisted Intervention\u2013MICCAI 2015: 18th International Conference.Springer;2015:234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12029-0_40"},{"key":"e_1_2_7_11_1","doi-asserted-by":"crossref","unstructured":"TangY YangD LiW et\u00a0al.Self\u2010supervised pre\u2010training of swin transformers for 3D medical image analysis. InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2022:20730\u201020740.","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"e_1_2_7_13_1","unstructured":"JorgeCM LiW BrownR et\u00a0al.MONAI: An open\u2010source framework for deep learning in healthcare.arXiv preprint arXiv:2211.02701.2022."},{"key":"e_1_2_7_14_1","unstructured":"KingmaDP BaJ:Adam: A method for stochastic optimization.International Conference on Learning Representations.2015."},{"key":"e_1_2_7_15_1","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy KP","year":"2012"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13950"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2021.3055199"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2600249"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3231428"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.17440","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T23:22:58Z","timestamp":1755904978000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.17440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,7]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1002\/mp.17440"],"URL":"https:\/\/doi.org\/10.1002\/mp.17440","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,7]]},"assertion":[{"value":"2024-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}