{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:18:43Z","timestamp":1758349123573,"version":"3.44.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04937-7_20","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:10Z","timestamp":1758260350000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generating Novel Brain Morphology by\u00a0Deforming Learned Templates"],"prefix":"10.1007","author":[{"given":"Alan Q.","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fangrui","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Bailey","family":"Trang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Abbasi","sequence":"additional","affiliation":[]},{"given":"Kilian","family":"Pohl","sequence":"additional","affiliation":[]},{"given":"Mert R.","family":"Sabuncu","sequence":"additional","affiliation":[]},{"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"B Billot","year":"2023","unstructured":"Billot, B., et al.: Synthseg: segmentation of brain mri scans of any contrast and resolution without retraining. Med. Image Anal. 86, 102789 (2023)","journal-title":"Med. Image Anal."},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neuroimage.2017.10.060","volume":"166","author":"C Blaiotta","year":"2018","unstructured":"Blaiotta, C., Freund, P., Cardoso, M.J., Ashburner, J.: Generative diffeomorphic modelling of large mri data sets for probabilistic template construction. Neuroimage 166, 117\u2013134 (2018)","journal-title":"Neuroimage"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"64747","DOI":"10.1109\/ACCESS.2021.3075608","volume":"9","author":"CK Chong","year":"2021","unstructured":"Chong, C.K., Ho, E.T.W.: Synthesis of 3d mri brain images with shape and texture generative adversarial deep neural networks. IEEE Access 9, 64747\u201364760 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3075608","journal-title":"IEEE Access"},{"issue":"6","key":"20_CR4","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/42.650882","volume":"16","author":"G Christensen","year":"1997","unstructured":"Christensen, G., Joshi, S., Miller, M.: Volumetric transformation of brain anatomy. IEEE Trans. Med. Imaging 16(6), 864\u2013877 (1997)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR5","unstructured":"Dalca, A., Rakic, M., Guttag, J., Sabuncu, M.: Learning conditional deformable templates with convolutional networks. In: NeurIPS, vol.\u00a032 (2019)"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Dey, N., Ren, M., Dalca, A.V., Gerig, G.: Generative adversarial registration for improved conditional deformable templates (2022)","DOI":"10.1109\/ICCV48922.2021.00390"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis (2021)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"20_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103278","volume":"97","author":"V Fernandez","year":"2024","unstructured":"Fernandez, V., Pinaya, W.H.L., Borges, P., Graham, M.S., Tudosiu, P.D., Vercauteren, T., Cardoso, M.J.: Generating multi-pathological and multi-modal images and labels for brain mri. Med. Image Anal. 97, 103278 (2024)","journal-title":"Med. Image Anal."},{"issue":"1","key":"20_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.media.2003.06.001","volume":"8","author":"KA Ganser","year":"2004","unstructured":"Ganser, K.A., Dickhaus, H., Metzner, R., Wirtz, C.R.: A deformable digital brain atlas system according to talairach and tournoux. Med. Image Anal. 8(1), 3\u201322 (2004)","journal-title":"Med. Image Anal."},{"key":"20_CR10","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks (2014)"},{"key":"20_CR11","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein gans (2017)"},{"key":"20_CR12","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 (2018)"},{"key":"20_CR13","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks (2018)","DOI":"10.1109\/CVPR.2017.632"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Jeanneret, G., Simon, L., Jurie, F.: Diffusion models for counterfactual explanations (2022)","DOI":"10.1007\/978-3-031-26293-7_14"},{"issue":"1","key":"20_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s41386-020-0736-6","volume":"46","author":"NR Karcher","year":"2021","unstructured":"Karcher, N.R., Barch, D.M.: The abcd study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46(1), 131\u2013142 (2021)","journal-title":"Neuropsychopharmacology"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Kim, B., Ye, J.C.: Diffusion deformable model for 4d temporal medical image generation (2022). https:\/\/arxiv.org\/abs\/2206.13295","DOI":"10.1007\/978-3-031-16431-6_51"},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Kwon, G., Han, C., Kim, D.: Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 118\u2013126. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_14","DOI":"10.1007\/978-3-030-32248-9_14"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Lakens, D.: Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and anovas. Front. Psychol. 4 (2013)","DOI":"10.3389\/fpsyg.2013.00863"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Madan, C.R.: Advances in studying brain morphology: the benefits of open-access data. Front. Hum. Neurosci. 11 (2017)","DOI":"10.3389\/fnhum.2017.00405"},{"key":"20_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103325","volume":"98","author":"W Peng","year":"2024","unstructured":"Peng, W., et al.: Metadata-conditioned generative models to synthesize anatomically-plausible 3d brain mris. Med. Image Anal. 98, 103325 (2024)","journal-title":"Med. Image Anal."},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Petersen, R.C., et al.: Alzheimer\u2019s disease neuroimaging initiative (adni): clinical characterization. Neurology 74(3), 201\u2013209 (2010)","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Brain imaging generation with latent diffusion models (2022)","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"20_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102723","volume":"84","author":"G Pombo","year":"2023","unstructured":"Pombo, G., et al.: Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3d deep generative models. Med. Image Anal. 84, 102723 (2023)","journal-title":"Med. Image Anal."},{"issue":"9","key":"20_CR25","first-page":"1453","volume":"26","author":"E Pulliam","year":"2011","unstructured":"Pulliam, E., Singleton, A.B.: The parkinson\u2019s progression markers initiative (ppmi): Study design and protocol. Mov. Disord. 26(9), 1453\u20131460 (2011)","journal-title":"Mov. Disord."},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Starck, S., Sideri-Lampretsa, V., Kainz, B., Menten, M., Mueller, T., Rueckert, D.: Diff-def: Diffusion-generated deformation fields for conditional atlases (2024). https:\/\/arxiv.org\/abs\/2403.16776","DOI":"10.1109\/TMI.2025.3595421"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Tudosiu, P.D., et al.: Morphology-preserving autoregressive 3d generative modelling of the brain. In: Simulation and Synthesis in Medical Imaging: 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings, p. 6678 (2022)","DOI":"10.1007\/978-3-031-16980-9_7"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The wu-minn human connectome project: an overview. NeuroImage 80, 62\u201379 (2013). mapping the Connectome","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"20_CR30","doi-asserted-by":"publisher","first-page":"53277","DOI":"10.1109\/ACCESS.2024.3387702","volume":"12","author":"AQ Wang","year":"2024","unstructured":"Wang, A.Q., Karaman, B.K., Kim, H., Rosenthal, J., Saluja, R., Young, S.I., Sabuncu, M.R.: A framework for interpretability in machine learning for medical imaging. IEEE Access 12, 53277\u201353292 (2024)","journal-title":"IEEE Access"},{"key":"20_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117546","volume":"226","author":"Y Wang","year":"2021","unstructured":"Wang, Y., et al.: Independent components of human brain morphology. Neuroimage 226, 117546 (2021)","journal-title":"Neuroimage"},{"key":"20_CR32","unstructured":"Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Asilomar Conference, vol.\u00a02, pp. 1398\u20131402 (2003)"},{"issue":"31","key":"20_CR33","doi-asserted-by":"publisher","first-page":"9661","DOI":"10.1523\/JNEUROSCI.2160-09.2009","volume":"29","author":"JL Whitwell","year":"2009","unstructured":"Whitwell, J.L.: Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J. Neurosci. 29(31), 9661\u20139664 (2009)","journal-title":"J. Neurosci."},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Woodland, M., et al.: Feature extraction for generative medical imaging evaluation: New evidence against an evolving trend. In: MICCAI 2024, pp. 87\u201397 (2024)","DOI":"10.1007\/978-3-031-72390-2_9"},{"key":"20_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552","volume":"58","author":"X Yi","year":"2019","unstructured":"Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Image Anal. 58, 101552 (2019)","journal-title":"Med. Image Anal."},{"key":"20_CR36","unstructured":"Zheng, J.Q., et al.: Deformation-recovery diffusion model (drdm): Instance deformation for image manipulation and synthesis (2024). https:\/\/arxiv.org\/abs\/2407.07295"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Zhuo, Y., Shen, Y.: Diffusereg: Denoising diffusion model for obtaining deformation fields in unsupervised deformable image registration (2024). https:\/\/arxiv.org\/abs\/2410.05234","DOI":"10.1007\/978-3-031-72069-7_56"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:23Z","timestamp":1758260363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","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":"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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}