{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:09:48Z","timestamp":1792793388161,"version":"4.5.0"},"reference-count":42,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health","award":["R01CA272991 and R21EB033994"],"award-info":[{"award-number":["R01CA272991 and R21EB033994"]}],"id":[{"id":"https:\/\/ror.org\/00372qc85","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In cone beam computed tomography (CBCT)-guided adaptive radiotherapy, rapid and precise segmentation of organs-at-risk (OARs) is essential for accurate dose verification and online replanning. The quality of CBCT images obtained with current onboard CBCT imagers and clinical imaging protocols, however, is often compromised by artifacts such as scatter and motion, particularly for thoracic CBCT scans. These artifacts not only degrade image contrast but also obscure anatomical boundaries, making accurate segmentation on CBCT images significantly more challenging compared to planning CT images. To address these persistent challenges, we propose a novel multi-channel conditional consistency diffusion model (MCCDM) for segmentation of OARs in thoracic CBCT images (CBCT-MCCDM), which harnesses its domain transfer capabilities to improve segmentation accuracy across different imaging modalities. By jointly training the MCCDM with CT images and their corresponding masks, our framework enables an end-to-end mapping learning process that generates accurate segmentation of OARs. This CBCT-MCCDM was used to delineate esophagus, heart, left and right lungs, and spinal cord on CBCT images from patients receiving radiation therapy. We quantitatively evaluated our approach by comparing model-generated contours with ground truth contours from 33 patients with lung or metastatic cancers treated with 5-fraction stereotactic body radiation therapy (SBRT), demonstrating its potential to enhance segmentation accuracy despite the presence of challenging CBCT artifacts. The proposed method was evaluated using average Dice similarity coefficients (DSC), sensitivity, specificity, 95th Percentile Hausdorff Distance (HD95), and mean surface distance (MSD) for each of the five OARs. The method achieved average DSC values of 0.82, 0.88, 0.95, 0.96, and 0.96 for the esophagus, heart, left lung, right lung, and spinal cord, respectively. Sensitivity values were 0.813, 0.922, 0.956, 0.958, and 0.929, respectively, while specificity values were 0.991, 0.994, 0.996, 0.996, and 0.995, respectively. We compared the proposed method with two state-of-art methods, CBCT-only method and U-Net, and demonstrated that the proposed CBCT-MCCDM method achieved superior performance across all metrics.<\/jats:p>","DOI":"10.1088\/2057-1976\/addac8","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T18:47:33Z","timestamp":1747766853000},"page":"045019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["CT-guided CBCT multi-organ segmentation using a multi-channel conditional consistency diffusion model for lung cancer radiotherapy"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6564-186X","authenticated-orcid":true,"given":"Xiaoqian","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7877-1900","authenticated-orcid":true,"given":"Richard L J","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyan","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph W","family":"Shelton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8297-9643","authenticated-orcid":true,"given":"Aparna H","family":"Kesarwala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"bpexaddac8bib1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"bpexaddac8bib2","doi-asserted-by":"publisher","first-page":"8661","DOI":"10.3390\/ijms22168661","article-title":"Current and future development in lung cancer diagnosis","volume":"22","author":"Nooreldeen","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"bpexaddac8bib3","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1093\/annonc\/mdv187","article-title":"2nd ESMO consensus conference in lung cancer: locally advanced stage III non-small-cell lung cancer","volume":"26","author":"Eberhardt","year":"2015","journal-title":"Annals of Oncology"},{"key":"bpexaddac8bib4","doi-asserted-by":"publisher","first-page":"iv1\u2013v21","DOI":"10.1093\/annonc\/mdx222","article-title":"Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up","volume":"28","author":"ESMO Guidelines Committee","year":"2017","journal-title":"Annals of Oncology"},{"key":"bpexaddac8bib5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1111\/resp.13870","article-title":"Radiotherapy treatment for lung cancer: current status and future directions","volume":"25","author":"Vinod","year":"2020","journal-title":"Respirology"},{"key":"bpexaddac8bib6","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1002\/1878-0261.12751","article-title":"Image guidance in radiation therapy for better cure of cancer","volume":"14","author":"Gregoire","year":"2020","journal-title":"Molecular Oncology"},{"key":"bpexaddac8bib7","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1049\/ipr2.12419","article-title":"Medical image segmentation using deep learning: a survey","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Proc."},{"key":"bpexaddac8bib8","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s00784-018-2561-4","article-title":"Factors influencing the effective dose associated with CBCT: a systematic review","volume":"23","author":"da Silva Moura","year":"2019","journal-title":"Clin. Oral Investigations"},{"key":"bpexaddac8bib9","doi-asserted-by":"publisher","first-page":"5130","DOI":"10.1002\/mp.15083","article-title":"Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation","volume":"48","author":"Dahiya","year":"2021","journal-title":"Med. Phys."},{"key":"bpexaddac8bib10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/acm2.13121","article-title":"A review on medical imaging synthesis using deep learning and its clinical applications","volume":"22","author":"Wang","year":"2021","journal-title":"Journal of Applied Clinical Medical Physics"},{"key":"bpexaddac8bib11","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bpexaddac8bib12","article-title":"Diffusion adversarial representation learning for self-supervised vessel segmentation","author":"Kim","year":"2023"},{"key":"bpexaddac8bib13","doi-asserted-by":"publisher","first-page":"816878","DOI":"10.1002\/mp.17328","article-title":"CBCT-based synthetic CT image generation using a diffusion model for CBCT-guided lung radiotherapy","volume":"51","author":"Chen","year":"2024","journal-title":"Med. Phys."},{"key":"bpexaddac8bib14","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","article-title":"A review of deep-learning-based medical image segmentation methods","volume":"13","author":"Liu","year":"2021","journal-title":"Sustainability"},{"key":"bpexaddac8bib15","doi-asserted-by":"publisher","first-page":"e6","DOI":"10.4108\/eai.12-4-2021.169184","article-title":"A review of medical image segmentation algorithms","volume":"7","author":"Ramesh","year":"2021","journal-title":"EAI Endorsed Transactions on Pervasive Health and Technology"},{"key":"bpexaddac8bib16","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"Journal of Medical Imaging and Radiation Oncology"},{"key":"bpexaddac8bib17","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1002\/mp.14906","article-title":"Deep learning based image registration and automatic segmentation of organs at risk in cone beam CT scans from high-dose radiation treatment of pancreatic cancer","volume":"48","author":"Han","year":"2021","journal-title":"Med. Phys."},{"key":"bpexaddac8bib18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.radonc.2019.11.021","article-title":"Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT","volume":"145","author":"Schreier","year":"2020","journal-title":"Radiother. Oncol."},{"key":"bpexaddac8bib19","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2020.614384","article-title":"Clinical enhancement in AI-based post-processed fast-scan low-dose CBCT for head and neck adaptive radiotherapy","volume":"3","author":"Chen","year":"2021","journal-title":"Front Artif Intell"},{"key":"bpexaddac8bib20","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1109\/TMI.2022.3154934","article-title":"One shot PACS: patient specific anatomic context and shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs","volume":"41","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexaddac8bib21","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: a survey","volume":"69","author":"Guan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"bpexaddac8bib22","first-page":"567","article-title":"Cone-beam computed tomography (CBCT) segmentation by adversarial learning domain adaptation","volume":"11769","author":"Jia","year":"2019","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"bpexaddac8bib23","article-title":"Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives","volume":"170","author":"Kumari","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"bpexaddac8bib24","doi-asserted-by":"publisher","first-page":"7063","DOI":"10.1002\/mp.15264","article-title":"Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy","volume":"48","author":"Dai","year":"2021","journal-title":"Med. Phys."},{"key":"bpexaddac8bib25","first-page":"3","article-title":"October. Source-free domain adaptation for medical image segmentation via prototype-anchored feature alignment and contrastive learning","author":"Yu","year":"2023"},{"key":"bpexaddac8bib26","doi-asserted-by":"publisher","first-page":"e84","DOI":"10.1002\/mp.12824","article-title":"Image guidance doses delivered during radiotherapy: quantification, management, and reduction: report of the AAPM therapy physics committee task group 180","volume":"45","author":"Ding","year":"2018","journal-title":"Med. Phys."},{"key":"bpexaddac8bib27","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bpexaddac8bib28","article-title":"Diffusion models for medical image analysis: a comprehensive survey","author":"Kazerouni","year":"2022"},{"key":"bpexaddac8bib29","doi-asserted-by":"publisher","first-page":"471\u201380","DOI":"10.1002\/mp.17463","article-title":"Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy","volume":"52","author":"Chen","year":"2025","journal-title":"Med. Phys."},{"key":"bpexaddac8bib30","first-page":"1\u201339","article-title":"Diffusion models: A comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"bpexaddac8bib31","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17068","article-title":"Full-dose whole-body PET synthesis from low-dose PET using high- efficiency denoising diffusion probabilistic model: PET consistency model","author":"Pan","year":"2024","journal-title":"Med. Phys."},{"key":"bpexaddac8bib32","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020"},{"key":"bpexaddac8bib33","article-title":"Consistency models","author":"Song","year":"2023"},{"key":"bpexaddac8bib34","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexaddac8bib35","doi-asserted-by":"publisher","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: the success of u-net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"bpexaddac8bib36","doi-asserted-by":"publisher","first-page":"5637","DOI":"10.1007\/s10462-020-09830-9","article-title":"Image segmentation evaluation: a survey of methods","volume":"53","author":"Wang","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"bpexaddac8bib37","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger"},{"key":"bpexaddac8bib38","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1002\/mp.14386","article-title":"CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy","volume":"47","author":"Liu","year":"2020","journal-title":"Med. Phys."},{"key":"bpexaddac8bib39","doi-asserted-by":"publisher","first-page":"13408","DOI":"10.1109\/TPAMI.2023.3289667","article-title":"Adaptive region-specific loss for improved medical image segmentation","volume":"45","author":"Chen","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"bpexaddac8bib40","doi-asserted-by":"publisher","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: a survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"bpexaddac8bib41","article-title":"Non gaussian denoising diffusion models","author":"Nachmani","year":"2021"},{"key":"bpexaddac8bib42","doi-asserted-by":"publisher","first-page":"4078","DOI":"10.1118\/1.3438081","article-title":"Stereotactic body radiation therapy: the report of AAPM Task Group 101","volume":"37","author":"Benedict","year":"2010","journal-title":"Med. Phys."}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T08:13:20Z","timestamp":1750407200000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/addac8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,6,20]]},"published-print":{"date-parts":[[2025,7,31]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/addac8","relation":{},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"CT-guided CBCT multi-organ segmentation using a multi-channel conditional consistency diffusion model for lung cancer radiotherapy","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-02-04","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-20","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-06-20","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}