{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:24:13Z","timestamp":1779416653355,"version":"3.53.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-026-03587-2","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:33:35Z","timestamp":1773063215000},"page":"703-710","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic skin image generation using a physics-based, object-to-image computational pipeline"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0007-4422-6801","authenticated-orcid":false,"given":"Elena","family":"Sizikova","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niloufar","family":"Saharkhiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Lago","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jana G.","family":"Delfino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aldo","family":"Badano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"3587_CR1","doi-asserted-by":"crossref","unstructured":"Kim A, Saharkhiz N, Sizikova E, Lago M, Sahiner B, Delfino J, Badano A (2024) S-SYNTH: knowledge-based, synthetic generation of skin images. In: MICCAI. Springer, pp 734\u2013744","DOI":"10.1007\/978-3-031-72384-1_69"},{"issue":"19","key":"3587_CR2","doi-asserted-by":"publisher","first-page":"4694","DOI":"10.3390\/cancers15194694","volume":"15","author":"RH Patel","year":"2023","unstructured":"Patel RH, Foltz EA, Witkowski A, Ludzik J (2023) Analysis of artificial intelligence-based approaches applied to non-invasive imaging for early detection of melanoma: a_asystematic review. Cancers 15(19):4694","journal-title":"Cancers"},{"issue":"10","key":"3587_CR3","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/S2589-7500(23)00130-9","volume":"5","author":"SW Menzies","year":"2023","unstructured":"Menzies SW, Sinz C, Menzies M, Lo SN, Yolland W, Lingohr J, Razmara M, Tschandl P, Guitera P, Scolyer RA et al (2023) Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial. Lancet Digital Health 5(10):679\u2013691","journal-title":"Lancet Digital Health"},{"issue":"12","key":"3587_CR4","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1089\/wound.2021.0091","volume":"11","author":"D Anisuzzaman","year":"2022","unstructured":"Anisuzzaman D, Wang C, Rostami B, Gopalakrishnan S, Niezgoda J, Yu Z (2022) Image-based artificial intelligence in wound assessment: a systematic review. Adv Wound Care 11(12):687\u2013709","journal-title":"Adv Wound Care"},{"issue":"1","key":"3587_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s10916-023-02029-9","volume":"48","author":"N Curti","year":"2024","unstructured":"Curti N, Merli Y, Zengarini C, Starace M, Rapparini L, Marcelli E, Carlini G, Buschi D, Castellani GC, Piraccini BM et al (2024) Automated prediction of photographic wound assessment tool in chronic wound images. J Med Syst 48(1):14","journal-title":"J Med Syst"},{"issue":"6","key":"3587_CR6","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s40257-024-00883-y","volume":"25","author":"EV Goessinger","year":"2024","unstructured":"Goessinger EV, Gottfrois P, Mueller AM, Cerminara SE, Navarini AA (2024) Image-based artificial intelligence in psoriasis assessment: The beginning of a new diagnostic era? Am J Clin Dermatol 25(6):861\u2013872","journal-title":"Am J Clin Dermatol"},{"issue":"12","key":"3587_CR7","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1111\/jdv.18354","volume":"36","author":"T Okamoto","year":"2022","unstructured":"Okamoto T, Kawai M, Ogawa Y, Shimada S, Kawamura T (2022) Artificial intelligence for the automated single-shot assessment of psoriasis severity. J Eur Acad Dermatol Venereol 36(12):2512\u20132515","journal-title":"J Eur Acad Dermatol Venereol"},{"issue":"12","key":"3587_CR8","doi-asserted-by":"publisher","first-page":"2268","DOI":"10.1111\/jdv.20031","volume":"38","author":"Q Li","year":"2024","unstructured":"Li Q, Yang Z, Chen K, Zhao M, Long H, Deng Y, Hu H, Jia C, Wu M, Zhao Z et al (2024) Human-multimodal deep learning collaboration in \u2018precise\u2019 diagnosis of lupus erythematosus subtypes and similar skin diseases. J Eur Acad Dermatol Venereol 38(12):2268\u20132279","journal-title":"J Eur Acad Dermatol Venereol"},{"key":"3587_CR9","doi-asserted-by":"crossref","unstructured":"Prestwood CA, Gibbs DC, Wolner Z, Stoff BK (2025) A review of artificial intelligence in dermatopathology: opportunities, challenges, and future directions. Dermatol Clin","DOI":"10.1016\/j.det.2025.05.006"},{"issue":"6","key":"3587_CR10","doi-asserted-by":"publisher","first-page":"3585","DOI":"10.1007\/s11831-024-10091-w","volume":"31","author":"S Asif","year":"2024","unstructured":"Asif S, Zhao M, Li Y, Tang F, Khan URS, Zhu Y (2024) Ai-based approaches for the diagnosis of mpox: challenges and future prospects. Arch Comput Methods Eng 31(6):3585\u20133617","journal-title":"Arch Comput Methods Eng"},{"key":"3587_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106624","volume":"155","author":"MK Hasan","year":"2023","unstructured":"Hasan MK, Ahamad MA, Yap CH, Yang G (2023) A survey, review, and future trends of skin lesion segmentation and classification. Comput Biol Med 155:106624","journal-title":"Comput Biol Med"},{"key":"3587_CR12","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: MICCAI. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3587_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102863","volume":"88","author":"Z Mirikharaji","year":"2023","unstructured":"Mirikharaji Z, Abhishek K, Bissoto A, Barata C, Avila S, Valle E, Celebi ME, Hamarneh G (2023) A survey on deep learning for skin lesion segmentation. Med Image Anal 88:102863","journal-title":"Med Image Anal"},{"key":"3587_CR14","doi-asserted-by":"crossref","unstructured":"Bozorgpour A, Sadegheih Y, Kazerouni A, Azad R, Merhof D (2023) Dermosegdiff: a boundary-aware segmentation diffusion model for skin lesion delineation. In: PRIME. Springer","DOI":"10.1007\/978-3-031-46005-0_13"},{"key":"3587_CR15","unstructured":"Ghorbani A, Natarajan V, Coz D, Liu Y (2020) Dermgan: Synthetic generation of clinical skin images with pathology. In: Machine learning for health workshop. PMLR, pp 155\u2013170"},{"key":"3587_CR16","unstructured":"Sagers LW, Diao JA, Groh M, Rajpurkar P, Adamson A, Manrai AK (2022) Improving dermatology classifiers across populations using images generated by large diffusion models. In: NeurIPS. Workshop on synthetic data for empowering ML research"},{"issue":"19","key":"3587_CR17","doi-asserted-by":"publisher","first-page":"3147","DOI":"10.3390\/diagnostics13193147","volume":"13","author":"TG Debelee","year":"2023","unstructured":"Debelee TG (2023) Skin lesion classification and detection using machine learning techniques: a systematic review. Diagnostics 13(19):3147","journal-title":"Diagnostics"},{"issue":"6","key":"3587_CR18","first-page":"472","volume":"67","author":"JE Gershenwald","year":"2017","unstructured":"Gershenwald JE, Scolyer RA, Hess KR, Sondak VK, Long GV, Ross MI, Lazar AJ, Faries MB, Kirkwood JM, McArthur GA et al (2017) Melanoma staging: evidence-based changes in the American joint committee on cancer eighth edition cancer staging manual. CA Cancer J Clin 67(6):472\u2013492","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"3587_CR19","first-page":"007","volume":"1","author":"E Sizikova","year":"2024","unstructured":"Sizikova E, Badal A, Delfino JG, Lago M, Nelson B, Saharkhiz N, Sahiner B, Zamzmi G, Badano A (2024) Synthetic data in radiological imaging: current state and future outlook. BJR Artif Intell 1(1):007","journal-title":"BJR Artif Intell"},{"key":"3587_CR20","unstructured":"Sagers LW, Diao JA, Melas-Kyriazi L, Groh M, Rajpurkar P, Adamson AS, Rotemberg V, Daneshjou R, Manrai AK (2023) Augmenting medical image classifiers with synthetic data from latent diffusion models. arXiv:2308.12453"},{"key":"3587_CR21","doi-asserted-by":"crossref","unstructured":"Oliveira DAB (2020) Controllable skin lesion synthesis using texture patches, b\u00e9zier curves and conditional GANs. In: ISBI. IEEE","DOI":"10.1109\/ISBI45749.2020.9098676"},{"key":"3587_CR22","doi-asserted-by":"crossref","unstructured":"Wang J, Chung Y, Ding Z, Hamm J (2024) From majority to minority: a diffusion-based augmentation for underrepresented groups in skin lesion analysis. In: MICCAI. Springer, pp 14\u201323","DOI":"10.1007\/978-3-031-77610-6_2"},{"key":"3587_CR23","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y et al (2023) Segment anything. In: ICCV, pp 4015\u20134026","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"3587_CR24","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.1109\/ACCESS.2020.3047258","volume":"9","author":"L Talavera-Martinez","year":"2020","unstructured":"Talavera-Martinez L, Bibiloni P, Gonzalez-Hidalgo M (2020) Hair segmentation and removal in dermoscopic images using deep learning. IEEE Access 9:2694\u20132704","journal-title":"IEEE Access"},{"issue":"1","key":"3587_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13721-024-00461-6","volume":"13","author":"D Bardou","year":"2024","unstructured":"Bardou D, Lv L, Medjadba Y, Zhang T, Chouhal O, Bounezra M, Saidi K, Bezza Y, Snani A (2024) GAD-VAE: generative adversarial disentanglement with variational autoencoders for hair removal in dermoscopy images. Netw Model Anal Health Inform Bioinform 13(1):1\u201318","journal-title":"Netw Model Anal Health Inform Bioinform"},{"key":"3587_CR26","doi-asserted-by":"publisher","first-page":"42610","DOI":"10.1109\/ACCESS.2021.3065701","volume":"9","author":"D Kim","year":"2021","unstructured":"Kim D, Hong B-W (2021) Unsupervised feature elimination via generative adversarial networks: application to hair removal in melanoma classification. IEEE Access 9:42610\u201342620","journal-title":"IEEE Access"},{"key":"3587_CR27","doi-asserted-by":"crossref","unstructured":"Aliaga C, Xia M, Xie X, Jarabo A, Braun G, Hery C (2023) A hyperspectral space of skin tones for inverse rendering of biophysical skin properties. In: Computer graphics forum. Wiley Online Library, pp 14887","DOI":"10.1111\/cgf.14887"},{"key":"3587_CR28","unstructured":"Top Derm. https:\/\/2.zoppoz.workers.dev:443\/https\/apps.apple.com\/in\/app\/top-derm\/id1505608766"},{"issue":"2","key":"3587_CR29","doi-asserted-by":"publisher","first-page":"30325","DOI":"10.2196\/30325","volume":"4","author":"MD Szeto","year":"2021","unstructured":"Szeto MD, Strock D, Anderson J, Sivesind TE, Vorwald VM, Rietcheck HR, Weintraub GS, Dellavalle RP (2021) Gamification and game-based strategies for dermatology education: narrative review. JMIR Dermatol 4(2):30325","journal-title":"JMIR Dermatol"},{"key":"3587_CR30","unstructured":"SideFX: SideFX: Houdini (2022) https:\/\/2.zoppoz.workers.dev:443\/https\/www.sidefx.com\/docs\/houdini\/index.html"},{"key":"3587_CR31","unstructured":"Mitsuba 3 Renderer (2022) https:\/\/2.zoppoz.workers.dev:443\/https\/mitsuba-renderer.org"},{"key":"3587_CR32","doi-asserted-by":"crossref","unstructured":"Sengupta A, Sharma D, Badano A (2021) Computational model of tumor growth for in silico trials. In: Medical imaging 2021: physics of medical imaging. SPIE","DOI":"10.1117\/12.2580787"},{"key":"3587_CR33","unstructured":"Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, Kittler H, Halpern A (2019) Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC)"},{"issue":"1","key":"3587_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5(1):1\u20139","journal-title":"Sci Data"},{"key":"3587_CR35","doi-asserted-by":"crossref","unstructured":"Azad R, Al-Antary MT, Heidari M, Merhof D (2022) Transnorm: transformer provides a strong spatial normalization mechanism for a deep segmentation model. IEEE Access","DOI":"10.1109\/ACCESS.2022.3211501"},{"key":"3587_CR36","unstructured":"Kinyanjui N, Odonga T, Cintas C, Codella N, Panda R, Sattigeri P, Varshney K (2019) Estimating skin tone and effects on classification performance in dermatology datasets. In: NeurIPS"},{"key":"3587_CR37","unstructured":"Colorimetry-Part C (2008) 4: Cie 1976 l* a* b* colour space. CIE Draft Standard DS"},{"key":"3587_CR38","doi-asserted-by":"crossref","unstructured":"Brooks T, Holynski A, Efros AA (2023) Instructpix2pix: learning to follow image editing instructions. In: CVPR, pp 18392\u201318402","DOI":"10.1109\/CVPR52729.2023.01764"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11548-026-03587-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11548-026-03587-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11548-026-03587-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T01:30:36Z","timestamp":1779413436000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/s11548-026-03587-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["3587"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s11548-026-03587-2","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"assertion":[{"value":"12 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article reflects the views of the authors and does not represent the views or policy of the U.S. Food and Drug Administration, the Department of Health and Human Services or the U.S. Government. The mention of commercial products, their sources or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclaimer"}}]}}