{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T15:31:06Z","timestamp":1758814266638,"version":"3.44.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049643","type":"print"},{"value":"9783032049650","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\/978-3-032-04965-0_57","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T08:05:30Z","timestamp":1758182730000},"page":"604-614","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Neural Cellular Automata: Application to\u00a0Modeling of\u00a0Contrast Enhancement in\u00a0Breast MRI"],"prefix":"10.1007","author":[{"given":"Daniel M.","family":"Lang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Osuala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronika","family":"Spieker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rickmer","family":"Braren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia A.","family":"Schnabel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"57_CR1","unstructured":"Aithal, S.K., Maini, P., Lipton, Z., Kolter, J.Z.: Understanding hallucinations in diffusion models through mode interpolation. Adv. Neural. Inf. Process. Syst. 37, 134614\u2013134644 (2025)"},{"key":"57_CR2","doi-asserted-by":"crossref","unstructured":"Deutges, M., Sadafi, A., Navab, N., Marr, C.: Neural cellular automata for lightweight, robust and explainable classification of white blood cell images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 693\u2013702. Springer (2024)","DOI":"10.1007\/978-3-031-72384-1_65"},{"key":"57_CR3","doi-asserted-by":"crossref","unstructured":"Elbatel, M., Kamnitsas, K., Li, X.: An organism starts with a single pix-cell: a neural cellular diffusion for high-resolution image synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 656\u2013666. Springer (2024)","DOI":"10.1007\/978-3-031-72378-0_61"},{"key":"57_CR4","doi-asserted-by":"crossref","unstructured":"Gao, Y., Reig, B., Heacock, L., Bennett, D.L., Heller, S.L., Moy, L.: Magnetic resonance imaging in screening of breast cancer. Radiol. Clin. North Am. 59(1), 85 (2020)","DOI":"10.1016\/j.rcl.2020.09.004"},{"key":"57_CR5","unstructured":"Garrucho, L., et\u00a0al.: MAMA-MIA: A large-scale multi-center breast cancer dce-mri benchmark dataset with expert segmentations. arXiv preprint arXiv:2406.13844 (2024)"},{"key":"57_CR6","doi-asserted-by":"crossref","unstructured":"Gilpin, W.: Cellular automata as convolutional neural networks. Phys. Rev. E 100(3), 032402 (2019)","DOI":"10.1103\/PhysRevE.100.032402"},{"key":"57_CR7","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. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"57_CR8","doi-asserted-by":"crossref","unstructured":"Kalkhof, J., Gonz\u00e1lez, C., Mukhopadhyay, A.: Med-NCA: Robust and lightweight segmentation with neural cellular automata. In: International Conference on Information Processing in Medical Imaging, pp. 705\u2013716. Springer (2023)","DOI":"10.1007\/978-3-031-34048-2_54"},{"key":"57_CR9","doi-asserted-by":"crossref","unstructured":"Kalkhof, J., Mukhopadhyay, A.: M3D-NCA: Robust 3D segmentation with built-in quality control. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 169\u2013178. Springer (2023)","DOI":"10.1007\/978-3-031-43898-1_17"},{"key":"57_CR10","doi-asserted-by":"crossref","unstructured":"Kuhl, C.K., Schrading, S., Strobel, K., Schild, H.H., Hilgers, R.D., Bieling, H.B.: Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI. J. Clin. Oncol. 32(22), 2304\u20132310 (2014)","DOI":"10.1200\/JCO.2013.52.5386"},{"key":"57_CR11","doi-asserted-by":"crossref","unstructured":"Leithner, D., Moy, L., Morris, E.A., Marino, M.A., Helbich, T.H., Pinker, K.: Abbreviated MRI of the breast: does it provide value? J. Magn. Res. Imaging 49(7), e85\u2013e100 (2019)","DOI":"10.1002\/jmri.26291"},{"key":"57_CR12","unstructured":"Lekadir, K., et\u00a0al.: FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. bmj 388 (2025)"},{"key":"57_CR13","doi-asserted-by":"crossref","unstructured":"Manzanera, O.E.M., et al.: Patient-specific 3d cellular automata nodule growth synthesis in lung cancer without the need of external data. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp.\u00a05\u20139. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433893"},{"key":"57_CR14","doi-asserted-by":"crossref","unstructured":"Mittal, A., Kalkhof, J., Mukhopadhyay, A., Bhavsar, A.: Medsegdiffnca: Diffusion models with neural cellular automata for skin lesion segmentation. arXiv preprint arXiv:2501.02447 (2025)","DOI":"10.1109\/CBMS65348.2025.00017"},{"issue":"2","key":"57_CR15","volume":"5","author":"A Mordvintsev","year":"2020","unstructured":"Mordvintsev, A., Randazzo, E., Niklasson, E., Levin, M.: Growing neural cellular automata. Distill 5(2), e23 (2020)","journal-title":"Growing neural cellular automata. Distill"},{"key":"57_CR16","doi-asserted-by":"crossref","unstructured":"M\u00fcller-Franzes, G., et al.: Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images. Radiology 307(3), e222211 (2023)","DOI":"10.1148\/radiol.222211"},{"key":"57_CR17","doi-asserted-by":"crossref","unstructured":"Osuala, R., et al.: Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks. arXiv preprint arXiv:2409.18872 (2024)","DOI":"10.1117\/1.JMI.12.S2.S22014"},{"key":"57_CR18","doi-asserted-by":"crossref","unstructured":"Osuala, R., et\u00a0al.: Towards learning contrast kinetics with multi-condition latent diffusion models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 713\u2013723. Springer (2024)","DOI":"10.1007\/978-3-031-72086-4_67"},{"key":"57_CR19","doi-asserted-by":"crossref","unstructured":"Pajouheshgar, E., Xu, Y., Zhang, T., S\u00fcsstrunk, S.: DyNCA: Real-time dynamic texture synthesis using neural cellular automata. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20742\u201320751 (2023)","DOI":"10.1109\/CVPR52729.2023.01987"},{"key":"57_CR20","unstructured":"Ranem, A., Kalkhof, J., Mukhopadhyay, A.: NCA-Morph: medical image registration with neural cellular automata. arXiv preprint arXiv:2410.22265 (2024)"},{"key":"57_CR21","doi-asserted-by":"crossref","unstructured":"Richardson, A.D., Antal, T., Blythe, R.A., Schumacher, L.J.: Learning spatio-temporal patterns with neural cellular automata. PLoS Comput. Biol. 20(4), e1011589 (2024)","DOI":"10.1371\/journal.pcbi.1011589"},{"key":"57_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.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, 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"},{"key":"57_CR23","doi-asserted-by":"crossref","unstructured":"Saha, A., et al.: A Machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. British J. Cancer 119(4), 508\u2013516 (2018)","DOI":"10.1038\/s41416-018-0185-8"},{"key":"57_CR24","doi-asserted-by":"crossref","unstructured":"Schreiter, H., et\u00a0al.: Virtual dynamic contrast enhanced breast MRI using 2D U-Net architectures. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, pp. 85\u201395. Springer (2024)","DOI":"10.1007\/978-3-031-77789-9_9"},{"key":"57_CR25","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. vol.\u00a02, pp. 1398\u20131402. IEEE (2003)"},{"key":"57_CR26","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"},{"key":"57_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"57_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Synthesis of contrast-enhanced breast MRI using T1-and multi-b-value DWI-based hierarchical fusion network with attention mechanism. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 79\u201388. Springer (2023)","DOI":"10.1007\/978-3-031-43990-2_8"}],"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\/978-3-032-04965-0_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:06:04Z","timestamp":1758233164000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04965-0_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049643","9783032049650"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04965-0_57","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":"All authors declare that they have no conflicts of interest.","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"}}]}}