{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:47:10Z","timestamp":1758268030099,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-04978-0_10","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:16:52Z","timestamp":1758212212000},"page":"100-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Scholz","sequence":"first","affiliation":[]},{"given":"Ayhan Can","family":"Erdur","sequence":"additional","affiliation":[]},{"given":"Robbie","family":"Holland","sequence":"additional","affiliation":[]},{"given":"Viktoria","family":"Ehm","sequence":"additional","affiliation":[]},{"given":"Jan C.","family":"Peeken","sequence":"additional","affiliation":[]},{"given":"Benedikt","family":"Wiestler","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"10_CR1","unstructured":"IXI Dataset \u2013 Brain Development, http:\/\/brain-development.org\/ixi-dataset\/"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"102799","DOI":"10.1016\/j.media.2023.102799","volume":"88","author":"S Cackowski","year":"2023","unstructured":"Cackowski, S., Barbier, E.L., Dojat, M., Christen, T.: Imunity: a generalizable vae-gan solution for multicenter mr image harmonization. Med. Image Anal. 88, 102799 (2023)","journal-title":"Med. Image Anal."},{"key":"10_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning, pp. 1597\u20131607. PMLR, November 2020"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.mri.2019.05.041","volume":"64","author":"BE Dewey","year":"2019","unstructured":"Dewey, B.E., Zhao, C., Reinhold, J.C., Carass, A., Fitzgerald, K.C., et al.: Deepharmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging 64, 160\u2013170 (2019)","journal-title":"Magn. Reson. Imaging"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Friedrich, P., Wolleb, J., Bieder, F., Durrer, A., Cattin, P.C.: Wdm: 3d wavelet diffusion models for high-resolution medical image synthesis. In: MICCAI Workshop on Deep Generative Models, pp. 11\u201321. Springer (2024)","DOI":"10.1007\/978-3-031-72744-3_2"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414\u20132423. IEEE, Las Vegas, NV, USA, June 2016","DOI":"10.1109\/CVPR.2016.265"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Gebre, R.K., Senjem, M.L., Raghavan, S., Schwarz, C.G., Gunter, J.L., et\u00a0al.: Cross\u2013scanner harmonization methods for structural MRI may need further work: a comparison study. NeuroImage 269, 119912 (2023)","DOI":"10.1016\/j.neuroimage.2023.119912"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"102904","DOI":"10.1016\/j.media.2023.102904","volume":"89","author":"R Gu","year":"2023","unstructured":"Gu, R., Wang, G., Lu, J., Zhang, J., Lei, W., et al.: Cddsa: contrastive domain disentanglement and style augmentation for generalizable medical image segmentation. Med. Image Anal. 89, 102904 (2023)","journal-title":"Med. Image Anal."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Hedges, E.P., Dimitrov, M., Zahid, U., Brito\u00a0Vega, B., Si, S., et\u00a0al.: Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream. NeuroImage 246, 118751 (2022)","DOI":"10.1016\/j.neuroimage.2021.118751"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Hu, F., Chen, A.A., Horng, H., Bashyam, V., Davatzikos, C., et\u00a0al.: Image harmonization: a review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. NeuroImage 274, 120125 (2023)","DOI":"10.1016\/j.neuroimage.2023.120125"},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-031-43901-8_2","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2023","author":"S Hu","year":"2023","unstructured":"Hu, S., Liao, Z., Xia, Y.: Devil is in channels: contrastive single domain generalization for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, pp. 14\u201323. Springer Nature Switzerland, Cham (2023)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Iglesias, J.E., Billot, B., Balbastre, Y., Tabari, A., Conklin, J., et\u00a0al.: Joint super-resolution and synthesis of 1\u00a0mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast. NeuroImage 237, 118206 (2021)","DOI":"10.1016\/j.neuroimage.2021.118206"},{"key":"10_CR13","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Kotovenko, D., Sanakoyeu, A., Lang, S., Ommer, B.: Content and style disentanglement for artistic style transfer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4422\u20134431 (2019)","DOI":"10.1109\/ICCV.2019.00452"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Kushol, R., Parnianpour, P., Wilman, A.H., Kalra, S., Yang, Y.H.: Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci. Rep. 13(1), 16791 (2023)","DOI":"10.1038\/s41598-023-43715-5"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., et\u00a0al.: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. medRxiv : the preprint server for health sciences, pp. 2019\u201312 (2019)","DOI":"10.1101\/2019.12.13.19014902"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Chen, C., Li, S., Li, Z., Qin, C., et\u00a0al.: Causality-inspired single-source domain generalization for medical image segmentation. IEEE Trans. Med.l Imaging 42(4), 1095\u20131106 (2023)","DOI":"10.1109\/TMI.2022.3224067"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"\u00d6zbey, M., Dalmaz, O., Dar, S.U., Bedel, H.A., \u00d6zturk, \u015e., et\u00a0al.: Unsupervised medical image translation with adversarial diffusion models. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3290149"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Preechakul, K., Chatthee, N., Wizadwongsa, S., Suwajanakorn, S.: Diffusion autoencoders: toward a meaningful and decodable representation. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10609\u201310619. IEEE, New Orleans, LA, USA, June 2022","DOI":"10.1109\/CVPR52688.2022.01036"},{"key":"10_CR20","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":"10_CR21","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models, October 2022"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"van Griethuysen, J.J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., et\u00a0al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104\u2013e107 (2017)","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Warrington, S., Ntata, A., Mougin, O., Campbell, J., Torchi, A., et\u00a0al.: ON-harmony: a resource for development and comparison of multimodal brain 3T MRI harmonisation approaches (2023)","DOI":"10.1101\/2023.06.16.545260"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Wolleb, J., Bieder, F., Sandk\u00fchler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 35\u201345. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_4"},{"key":"10_CR25","doi-asserted-by":"publisher","first-page":"107039","DOI":"10.1016\/j.neunet.2024.107039","volume":"184","author":"M Wu","year":"2025","unstructured":"Wu, M., Zhang, L., Yap, P.T., Zhu, H., Liu, M.: Disentangled latent energy-based style translation: an image-level structural mri harmonization framework. Neural Netw. 184, 107039 (2025)","journal-title":"Neural Netw."},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, F., Wu, Z., Wang, L., Lin, W., Xia, S., et\u00a0al.: Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 475\u2013483. Springer (2019)","DOI":"10.1007\/978-3-030-32251-9_52"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"10_CR28","doi-asserted-by":"publisher","first-page":"102285","DOI":"10.1016\/j.compmedimag.2023.102285","volume":"109","author":"L Zuo","year":"2023","unstructured":"Zuo, L., Liu, Y., Xue, Y., Dewey, B.E., Remedios, S.W., et al.: Haca3: a unified approach for multi-site mr image harmonization. Comput. Med. Imaging Graph. 109, 102285 (2023)","journal-title":"Comput. Med. Imaging Graph."},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Zuo, L., Liu, Y., Xue, Y., Han, S., Bilgel, M., et\u00a0al.: Disentangling a single mr modality. In: MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, pp. 54\u201363. Springer (2022)","DOI":"10.1007\/978-3-031-17027-0_6"}],"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\/978-3-032-04978-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:05:50Z","timestamp":1758233150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 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"}}]}}