{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:07:46Z","timestamp":1758672466830,"version":"3.44.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032058249","type":"print"},{"value":"9783032058256","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05825-6_9","type":"book-chapter","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T06:40:59Z","timestamp":1758609659000},"page":"93-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusing the\u00a0Blind Spot: Uterine MRI Synthesis with\u00a0Diffusion Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8636-7986","authenticated-orcid":false,"given":"Johanna P.","family":"M\u00fcller","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anika","family":"Knupfer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Bl\u00f6ss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edoardo Berardi","family":"Vittur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-5023","authenticated-orcid":false,"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-3500","authenticated-orcid":false,"given":"Jana","family":"Hutter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Baugh, M., et al.: Image-conditioned diffusion models for medical anomaly detection. In: International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, pp. 117\u2013127. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-73158-7_11"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Behrendt, F., et al.: Leveraging the mahalanobis distance to enhance unsupervised brain MRI anomaly detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 394\u2013404. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-72120-5_37"},{"key":"9_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108794","volume":"178","author":"E Bone\u0161","year":"2024","unstructured":"Bone\u0161, E., Gergolet, M., Bohak, C., Lesar, \u017d, Marolt, M.: Automatic segmentation and alignment of uterine shapes from 3D ultrasound data. Comput. Biol. Med. 178, 108794 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108794","journal-title":"Comput. Biol. Med."},{"key":"9_CR4","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments (2020)"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Dombrowski, M., Zhang, W., Cechnicka, S., Reynaud, H., Kainz, B.: Image generation diversity issues and how to tame them. In: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pp. 3029\u20133039 (2025)","DOI":"10.1109\/CVPR52734.2025.00288"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Dorjsembe, Z., Pao, H.K., Odonchimed, S., Xiao, F.: Conditional diffusion models for semantic 3d brain MRI synthesis. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.36227\/techrxiv.23723787"},{"key":"9_CR7","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR8","unstructured":"Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. In: Proceedings of NeurIPS (2022)"},{"key":"9_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni, A., et al.: Diffusion models in medical imaging: a comprehensive survey. Med. Image Anal. 88, 102846 (2023)","journal-title":"Med. Image Anal."},{"issue":"1","key":"9_CR10","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41597-024-03244-w","volume":"11","author":"D Li","year":"2024","unstructured":"Li, D., Zhang, T., Xu, L., et al.: Multi-center annotated MRI dataset and benchmark for uterine myoma segmentation and classification. Sci. Data 11(1), 192 (2024). https:\/\/doi.org\/10.1038\/s41597-024-03244-w","journal-title":"Sci. Data"},{"key":"9_CR11","unstructured":"Liu, C., Shah, A., Bai, W., Arcucci, R.: Utilizing synthetic data for medical vision-language pre-training: bypassing the need for real images. arXiv preprint arXiv:2310.07027 (2023)"},{"issue":"1","key":"9_CR12","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1038\/s41597-024-03170-x","volume":"11","author":"H Pan","year":"2024","unstructured":"Pan, H., et al.: Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations. Sci. Data 11(1), 410 (2024)","journal-title":"Sci. Data"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H., Tudosiu, P.D., Dafflon, J., Da\u00a0Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., Cardoso, M.J.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models, pp. 117\u2013126. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"9_CR14","unstructured":"von Platen, P., et al.: Diffusers: state-of-the-art diffusion models (2022). https:\/\/github.com\/huggingface\/diffusers"},{"issue":"1","key":"9_CR15","doi-asserted-by":"publisher","first-page":"28435","DOI":"10.1038\/s41598-024-79602-w","volume":"14","author":"M Pozzi","year":"2024","unstructured":"Pozzi, M., et al.: Generating and evaluating synthetic data in digital pathology through diffusion models. Sci. Rep. 14(1), 28435 (2024)","journal-title":"Sci. Rep."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Reynaud, H., et al.: Echonet-synthetic: privacy-preserving video generation for safe medical data sharing. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 285\u2013295. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-72104-5_28"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Webber, G., Reader, A.J.: Diffusion models for medical image reconstruction. BJR| Artif. Intell. 1(1), ubae013 (2024)","DOI":"10.1093\/bjrai\/ubae013"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Yang, Y., Fu, H., Aviles-Rivero, A.I., Sch\u00f6nlieb, C.B., Zhu, L.: Diffmic: dual-guidance diffusion network for medical image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 95\u2013105. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43987-2_10","DOI":"10.1007\/978-3-031-43987-2_10"}],"container-title":["Lecture Notes in Computer Science","Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05825-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T06:41:10Z","timestamp":1758609670000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05825-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"ISBN":["9783032058249","9783032058256"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05825-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,24]]},"assertion":[{"value":"24 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGM4MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Deep Generative Models","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dgm4miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgm4miccai.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}