{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:39:06Z","timestamp":1775043546560,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721038","type":"print"},{"value":"9783031721045","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72104-5_9","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"88-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Anatomically-Controllable Medical Image Generation with\u00a0Segmentation-Guided Diffusion Models"],"prefix":"10.1007","author":[{"given":"Nicholas","family":"Konz","sequence":"first","affiliation":[]},{"given":"Yuwen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Maciej A.","family":"Mazurowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.neunet.2023.02.019","volume":"161","author":"Y Tian","year":"2023","unstructured":"Tian, Y., Yu, X., Fu, S.: Partial label learning: taxonomy, analysis and outlook. Neural Networks 161, 708\u2013734 (2023)","journal-title":"Neural Networks"},{"issue":"2","key":"9_CR2","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1007\/s10278-022-00755-z","volume":"36","author":"S Cao","year":"2023","unstructured":"Cao, S., Konz, N., Duncan, J., Mazurowski, M.A.: Deep learning for breast MRI style transfer with limited training data. J. Digit. Imaging 36(2), 666\u2013678 (2023)","journal-title":"J. Digit. Imaging"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Fernandez, V., Pinaya, W.H.L., Borges, P., Graham, M.S., Vercauteren, T., Cardoso, M.J.: A 3D generative model of pathological multi-modal MR images and segmentations. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) Deep Generative Models, pp. 132\u2013142. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-53767-7_13","DOI":"10.1007\/978-3-031-53767-7_13"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Fernandez, V., et al.: Can segmentation models be trained with fully synthetically generated data? In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds.) International Workshop on Simulation and Synthesis in Medical Imaging, pp. 79\u201390. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16980-9_8","DOI":"10.1007\/978-3-031-16980-9_8"},{"issue":"2","key":"9_CR6","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/s00259-023-06417-8","volume":"51","author":"K Gong","year":"2023","unstructured":"Gong, K., Johnson, K., El Fakhri, G., Li, Q., Pan, T.: PET image denoising based on denoising diffusion probabilistic model. Eur. J. Nucl. Med. Mol. Imaging 51(2), 358\u2013368 (2023)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"9_CR8","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"9_CR9","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_CR10","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021)"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"102846","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_CR12","doi-asserted-by":"publisher","first-page":"7303","DOI":"10.1038\/s41598-023-34341-2","volume":"13","author":"F Khader","year":"2023","unstructured":"Khader, F., et al.: Denoising diffusion probabilistic models for 3D medical image generation. Sci. Rep. 13(1), 7303 (2023)","journal-title":"Sci. Rep."},{"key":"9_CR13","unstructured":"Konz, N., Mazurowski, M.A.: Reverse engineering breast MRIs: predicting acquisition parameters directly from images. In: Medical Imaging with Deep Learning (2023)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Lew, C.O., et al.: A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI. Sci. Rep. 14(1), 5383 (2024). https:\/\/api.semanticscholar.org\/CorpusID:268251677","DOI":"10.1038\/s41598-024-54048-2"},{"key":"9_CR15","unstructured":"Lyu, Q., Wang, G.: Conversion between CT and MRI images using diffusion and score-matching models. arXiv preprint arXiv:2209.12104 (2022)"},{"key":"9_CR16","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162\u20138171. PMLR (2021)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Pinaya, W.H., et\u00a0al.: Fast unsupervised brain anomaly detection and segmentation with diffusion models. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 705\u2013714. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_67","DOI":"10.1007\/978-3-031-16452-1_67"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Pinaya, W.H., et al.: Brain imaging generation with latent diffusion models. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) Deep Generative Models. DGM4MICCAI 2022. LNCS, vol. 13609, pp. 117\u2013126. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_12","DOI":"10.1007\/978-3-031-18576-2_12"},{"issue":"1","key":"9_CR20","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1038\/s41597-020-00715-8","volume":"7","author":"B Rister","year":"2020","unstructured":"Rister, B., Yi, D., Shivakumar, K., Nobashi, T., Rubin, D.L.: CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Sci. Data 7(1), 381 (2020)","journal-title":"Sci. Data"},{"key":"9_CR21","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 (CVPR), pp. 10684\u201310695, June 2022","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Proceedings, Part III 18, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"9_CR23","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/s41416-018-0185-8","volume":"119","author":"A Saha","year":"2018","unstructured":"Saha, A., et al.: A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br. J. Cancer 119(4), 508\u2013516 (2018)","journal-title":"Br. J. Cancer"},{"key":"9_CR24","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=St1giarCHLP"},{"key":"9_CR25","unstructured":"Wang, T., et al.: Pretraining is all you need for image-to-image translation. arXiv preprint arXiv:2205.12952 (2022)"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Wolleb, J., Bieder, F., Sandk\u00fchler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 13438, pp. 35\u201345. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_4","DOI":"10.1007\/978-3-031-16452-1_4"},{"key":"9_CR27","unstructured":"Wolleb, J., Sandk\u00fchler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. In: International Conference on Medical Imaging with Deep Learning, pp. 1336\u20131348. PMLR (2022)"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M., Duncan, J.S.: Unsupervised domain adaptation via disentangled representations: application to cross-modality liver segmentation. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, 13\u201317 October 2019, Proceedings, Part II 22, vol. 11765, pp. 255\u2013263. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_29","DOI":"10.1007\/978-3-030-32245-8_29"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72104-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:04:36Z","timestamp":1733562276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72104-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721038","9783031721045"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72104-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}