{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:01:01Z","timestamp":1780354861853,"version":"3.54.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032234957","type":"print"},{"value":"9783032234964","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-23496-4_2","type":"book-chapter","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:12:04Z","timestamp":1780351924000},"page":"14-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ENSAM: An\u00a0Efficient Foundation Model for\u00a0Interactive Segmentation of\u00a03D Medical Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2654-4553","authenticated-orcid":false,"given":"Elias","family":"Stenhede","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4207-6278","authenticated-orcid":false,"given":"Agnar Martin","family":"Bj\u00f8rnstad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0422-2255","authenticated-orcid":false,"given":"Arian","family":"Ranjbar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"2_CR1","unstructured":"Aher, V., Villa, E.S., Mosquera, L.V.G., Torres, L.F.T., Verma, V.K., Ord\u00f3\u00f1ez, S.A.C.: MobileSeg3D: A Lightweight Framework for Multi-Modality 3D Medical Image Segmentation (Jun 2025). https:\/\/openreview.net\/forum?id=eqVsBeWMbG"},{"key":"2_CR2","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization (2016). https:\/\/arxiv.org\/abs\/1607.06450"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Bassi, P.R., et al.: Touchstone benchmark: are we on the right way for evaluating ai algorithms for medical segmentation? Adv. Neural. Inf. Process. Syst. 37, 15184\u201315201 (2024)","DOI":"10.52202\/079017-0485"},{"key":"2_CR4","unstructured":"Bernstein, J., Newhouse, L.: Modular duality in deep learning. arXiv preprint arXiv:2410.21265 (2024)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rck, \u00c5., Bowie, C.: An iterative algorithm for computing the best estimate of an orthogonal matrix. SIAM J. Numer. Anal. 8(2), 358\u2013364 (1971)","DOI":"10.1137\/0708036"},{"key":"2_CR6","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)"},{"key":"2_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Du, Y., Bai, F., Huang, T., Zhao, B.: Segvol: Universal and interactive volumetric medical image segmentation. In: Advances in Neural Information Processing Systems, vol.\u00a037, pp. 110746\u2013110783 (2024)","DOI":"10.52202\/079017-3516"},{"key":"2_CR9","unstructured":"Fabian, I., et al.: nninteractive: Redefining 3D promptable segmentation. arXiv preprint arXiv:2503.08373 (2025)"},{"key":"2_CR10","unstructured":"Friedetzki, T., Haberzettl, L., Buttman, R., Puppe, F., Krenzer, A.: iMedSTAM: Interactive Segmentation and Tracking Anything in 3D Medical Images and Videos (Jun 2025). https:\/\/openreview.net\/forum?id=1tsCloFWFT"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"He, Y., et al.: VISTA3D: a unified segmentation foundation model for 3D medical imaging. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPR52734.2025.01943"},{"key":"2_CR12","unstructured":"Huang, C., Huang, J., Wang, L.: From Single-Round to Sequential: Building Stateful Interactive Segmentation with SegVol and GRU Corrector (Jun 2025). https:\/\/openreview.net\/forum?id=45TSSNV3sJ"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","DOI":"10.1038\/s41592-020-01008-z"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., J\u00e4ger, P.F.: nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"2_CR15","unstructured":"Ji, J., Lin, T., Xiong, J., Han, T.: Enhancing a 3D Foundation Model with Gaussian Sampling for Interactive Biomedical Image Segmentation (Jun 2025). https:\/\/openreview.net\/forum?id=CLk0KhDXgm"},{"key":"2_CR16","unstructured":"Jo, S., Choi, A., Hong, J.H.: GAMT: A Geometry-Aware, Multi-view, Training-free Segmentation Framework for Foundation Models in Medical Imaging (Jun 2025). https:\/\/openreview.net\/forum?id=DeeoLKgCVU"},{"key":"2_CR17","unstructured":"Jordan, K.: 94 % on cifar-10 in 3.29 seconds on a single gpu. arXiv preprint arXiv:2404.00498 (2024)"},{"key":"2_CR18","unstructured":"Jordan, K., Jin, Y., Boza, V., You, J., Cesista, F., Newhouse, L., Bernstein, J.: Muon: an optimizer for hidden layers in neural networks (2024). https:\/\/kellerjordan.github.io\/posts\/muon\/"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support, pp. 277\u2013289. Springer (2013)","DOI":"10.1007\/978-1-4614-7657-3_19"},{"key":"2_CR20","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"key":"2_CR21","unstructured":"Lin, J., zhengdong, Ma, Z., Xiao, Y., Fu, H., Pan, Y.: Enhanced SAM-Med3D: A Robust Solution for 3D Medical Image Segmentation with Advanced Post-processing (Jun 2025). https:\/\/openreview.net\/forum?id=Y3zTAf99Vr"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.07.005","DOI":"10.1016\/j.media.2017.07.005"},{"key":"2_CR23","unstructured":"Liu, J., et al.: Muon is scalable for LLM training (2025)"},{"key":"2_CR24","unstructured":"Loshchilov, I., Hsieh, C.P., Sun, S., Ginsburg, B.: NGPT: Normalized transformer with representation learning on the hypersphere. In: Proceedings of the International Conference on Learning Representations (ICLR) (2025). arXiv:2410.01131 [cs.LG]"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)","DOI":"10.1016\/j.media.2021.102035"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15, 654 (2024)","DOI":"10.1038\/s41467-024-44824-z"},{"key":"2_CR27","unstructured":"Ma, J., et al.: Efficient medsams: Segment anything in medical images on laptop. arXiv:2412.16085 (2024)"},{"key":"2_CR28","unstructured":"Ma, J., et al.: Medsam2: Segment anything in 3d medical images and videos. arXiv preprint arXiv:2504.03600 (2025)"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: International MICCAI Brainlesion Workshop, pp. 311\u2013320. Springer (2018)","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y., Xu, D.: Automated head and neck tumor segmentation from 3D pet\/ct hecktor 2022 challenge report. In: 3D Head and Neck Tumor Segmentation in PET\/CT Challenge, pp. 31\u201337. Springer (2022)","DOI":"10.1007\/978-3-031-27420-6_2"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Myronenko, A., Yang, D., He, Y., Xu, D.: Aorta segmentation from 3D CT in MICCAI seg. a. 2023 challenge. In: MICCAI Challenge on Segmentation of the Aorta, pp. 13\u201318. Springer (2023)","DOI":"10.1007\/978-3-031-53241-2_2"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Myronenko, A., Yang, D., He, Y., Xu, D.: Automated 3D segmentation of kidneys and tumors in MICCAI kits 2023 challenge. In: International Challenge on Kidney and Kidney Tumor Segmentation, pp.\u00a01\u20137. Springer (2023)","DOI":"10.1007\/978-3-031-54806-2_1"},{"key":"2_CR33","unstructured":"Ndir, T.C., Pfefferle, A., Schirrmeister, R.T.: Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation (Jun 2025). https:\/\/openreview.net\/forum?id=EnSf2D1blH"},{"key":"2_CR34","unstructured":"Ostmeier, S., Axelrod, B., Moseley, M.E., Chaudhari, A., Langlotz, C.: LieRE: Generalizing Rotary Position Encodings (2025). arXiv:2406.10322"},{"key":"2_CR35","unstructured":"Qayyum, A., Mazher, M., Niederer, S.: Exploring Foundation Model Adaptations for 3D Medical Imaging: Prompt-Based Segmentation with xLSTM network (Jun 2025). https:\/\/openreview.net\/forum?id=eMskbK0uSY"},{"key":"2_CR36","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8) (2019)"},{"key":"2_CR37","unstructured":"Ravi, N., et al.: Sam 2: Segment anything in images and videos. In: International Conference on Learning Representations (2025)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B., Liu, Y.: RoFormer: Enhanced Transformer with Rotary Position Embedding (2023). arXiv:2104.09864.","DOI":"10.1016\/j.neucom.2023.127063"},{"key":"2_CR39","unstructured":"Touvron, H., et\u00a0al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Sam-med3d: Towards general-purpose segmentation models for volumetric medical images. arXiv preprint arXiv:2310.15161 (2024)","DOI":"10.1007\/978-3-031-91721-9_4"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3(7), 100543 (2022)","DOI":"10.1016\/j.patter.2022.100543"},{"key":"2_CR42","unstructured":"Zhang, B., Sennrich, R.: Root mean square layer normalization (2019). https:\/\/arxiv.org\/abs\/1910.07467"},{"key":"2_CR43","unstructured":"Zhang, Z., Yu, Y., Xue, Y.: Rethinking RoI Strategy in Interactive 3D Segmentation for Medical Images (Jun 2025). https:\/\/openreview.net\/forum?id=jospESnUL9"}],"container-title":["Lecture Notes in Computer Science","Foundation Models for 3D Biomedical Image Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23496-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:12:10Z","timestamp":1780351930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23496-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032234957","9783032234964"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23496-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MedSegFM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Challenge on Foundation Models for 3D Biomedical Image Segmentation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nashville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"11 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medsegfm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/openreview.net\/group?id=thecvf.com\/CVPR\/2025\/Workshop\/MedSegFM#tab-active-submissions","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}