{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:08:43Z","timestamp":1780589323658,"version":"3.54.1"},"reference-count":32,"publisher":"IOP Publishing","issue":"24","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"Ministry of Economy, Employment and Tourism of the State of Baden-W\u00a8urttemberg","award":["BW1 1523\/02"],"award-info":[{"award-number":["BW1 1523\/02"]}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"crossref","award":["13GW0388A"],"award-info":[{"award-number":["13GW0388A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,12,21]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:italic>\n . We present seg2med (segmentation-to-medical images), a modular framework for anatomy-driven multimodal medical image synthesis. The system integrates three components to enable high-fidelity, cross-modality generation of computed tomography (CT) and magnetic resonance (MR) images based on structured anatomical priors.\n Approach<\/jats:italic>\n . First, anatomical maps are independently derived from three sources: real patient data, extended cardiac-torso (XCAT) digital phantoms, and anatomies-synthetic subjects created by combining organs from multiple patients. Second, we introduce PhysioSynth, a modality-specific simulator that converts anatomical masks into imaging-like prior volumes using tissue-dependent parameters (e.g. HU,\n T<\/jats:italic>\n 1,\n T<\/jats:italic>\n 2,\n \u03c1<\/jats:italic>\n ) and modality-specific signal models. It supports simulation of CT and multiple MR sequences, including gradient-echo, SPACE, and volumetric interpolated breath-hold examination. Third, the synthesized anatomical priors are used to train 2-channel conditional denoising diffusion probabilistic models, which take the anatomical prior as a structural condition alongside the noisy image, enabling it to generate high-quality, structurally aligned images within its modality.\n Main results<\/jats:italic>\n . The framework achieves a structural similarity index measure (SSIM) of\n \n \n \n <\/jats:tex-math>\n \n \n 0.94<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.02<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n for CT and\n \n \n \n <\/jats:tex-math>\n \n \n 0.82<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.12<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n for MR images compared to real patient data, and\n \n \n \n <\/jats:tex-math>\n \n \n 0.78<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.04<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n FSIM for simulated CT from XCAT. The generative quality is further supported by a Fr\u00e9chet inception distance of 20.20 for CT synthesis. In modality conversion tasks, seg2med attains SSIM scores of\n \n \n \n <\/jats:tex-math>\n \n \n 0.91<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.03<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n (MR\u2009\n \n \n \n <\/jats:tex-math>\n \n \n \u2192<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n \u2009CT) and\n \n \n \n <\/jats:tex-math>\n \n \n 0.77<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.04<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n (CT\u2009\n \n \n \n <\/jats:tex-math>\n \n \n \u2192<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n \u2009MR).\n Significance<\/jats:italic>\n . In anatomical fidelity evaluation, synthetic CT images achieve a mean Dice coefficient exceeding 0.90 for 11 key abdominal organs, and over 0.80 for 34 of 59 total organs. These results underscore seg2med\u2019s utility in cross-modality image synthesis, dataset augmentation, and anatomy-aware AI development in medical imaging.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/ae2738","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T23:03:21Z","timestamp":1764716601000},"page":"245015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["seg2med: a bridge from artificial anatomy to multimodal medical images"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8730-8811","authenticated-orcid":true,"given":"Zeyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0617-4816","authenticated-orcid":true,"given":"Zhilin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8587-8894","authenticated-orcid":true,"given":"Yipeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-8481","authenticated-orcid":true,"given":"Anika","family":"Strittmatter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0036-0228","authenticated-orcid":false,"given":"Anish","family":"Raj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7344-7655","authenticated-orcid":true,"given":"Ahmad","family":"Allababidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7069-5181","authenticated-orcid":false,"given":"Johann S","family":"Rink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-1394","authenticated-orcid":false,"given":"Frank G","family":"Z\u00f6llner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"pmbae2738bib1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-66535-6_13","type":"preprint","article-title":"MRI scan synthesis methods based on clustering and pix2pix","author":"Baldini","year":"2024"},{"key":"pmbae2738bib2","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1007\/s11548-021-02372-7","type":"journal-article","article-title":"Generation of annotated multimodal ground truth datasets for abdominal medical image registration","volume":"16","author":"Bauer","year":"2021","journal-title":"Int. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-07-13","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-01","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-15","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}