{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T23:48:17Z","timestamp":1780962497636,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031824838","type":"print"},{"value":"9783031824845","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-82484-5_25","type":"book-chapter","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:05:05Z","timestamp":1741082705000},"page":"339-352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Gaussian Mixture Model for\u00a0Unsupervised Image Segmentation"],"prefix":"10.1007","author":[{"given":"Matthias","family":"Schwab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnes","family":"Mayr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Haltmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"issue":"5","key":"25_CR1","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbel\u00e1ez","year":"2011","unstructured":"Arbel\u00e1ez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898\u2013916 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.161","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s10462-011-9268-0","volume":"41","author":"M Balafar","year":"2014","unstructured":"Balafar, M.: Gaussian mixture model based segmentation methods for brain MRI images. Artif. Intell. Rev. 41, 429\u2013439 (2014). https:\/\/doi.org\/10.1007\/s10462-011-9268-0","journal-title":"Artif. Intell. Rev."},{"issue":"5","key":"25_CR3","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603\u2013619 (2002). https:\/\/doi.org\/10.1109\/34.1000236","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"25_CR4","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","volume":"128","author":"A Kuznetsova","year":"2020","unstructured":"Kuznetsova, A., et al.: The open images dataset V4. Int. J. Comput. Vision 128(7), 1956\u20131981 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01316-z","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"25_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1\u201322 (1977). https:\/\/doi.org\/10.1111\/j.2517-6161.1977.tb01600.x","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"Gruber, N., Schwab, J., Court, S., Gizewski, E., Haltmeier, M.: Variational multichannel multiclass segmentation using unsupervised lifting with CNNs. arXiv preprint arXiv:2302.02214 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.02214","DOI":"10.48550\/arXiv.2302.02214"},{"key":"25_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"25_CR8","doi-asserted-by":"publisher","unstructured":"Liu, J., et al.: Myocardium segmentation combining T2 and DE MRI using multi-component bivariate Gaussian mixture model. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 886\u2013889. IEEE (2014). https:\/\/doi.org\/10.1109\/ISBI.2014.6868013","DOI":"10.1109\/ISBI.2014.6868013"},{"issue":"11","key":"25_CR9","doi-asserted-by":"publisher","first-page":"2650","DOI":"10.1109\/TBME.2017.2657656","volume":"64","author":"J Liu","year":"2017","unstructured":"Liu, J., et al.: Myocardium segmentation from DE MRI using multicomponent Gaussian mixture model and coupled level set. IEEE Trans. Biomed. Eng. 64(11), 2650\u20132661 (2017). https:\/\/doi.org\/10.1109\/TBME.2017.2657656","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017). https:\/\/doi.org\/10.48550\/arXiv.1711.05101","DOI":"10.48550\/arXiv.1711.05101"},{"issue":"19","key":"25_CR11","doi-asserted-by":"publisher","first-page":"8802","DOI":"10.3390\/app11198802","volume":"11","author":"I Papadeas","year":"2021","unstructured":"Papadeas, I., Tsochatzidis, L., Amanatiadis, A., Pratikakis, I.: Real-time semantic image segmentation with deep learning for autonomous driving: a survey. Appl. Sci. 11(19), 8802 (2021). https:\/\/doi.org\/10.3390\/app11198802","journal-title":"Appl. Sci."},{"key":"25_CR12","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"key":"25_CR13","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(85), 2825\u20132830 (2011). http:\/\/jmlr.org\/papers\/v12\/pedregosa11a.html"},{"issue":"2","key":"25_CR14","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/42.563663","volume":"16","author":"JC Rajapakse","year":"1997","unstructured":"Rajapakse, J.C., Giedd, J.N., Rapoport, J.L.: Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imaging 16(2), 176\u2013186 (1997). https:\/\/doi.org\/10.1109\/42.563663","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"27","key":"25_CR15","doi-asserted-by":"publisher","DOI":"10.4108\/eai.12-4-2021.169184","volume":"7","author":"K Ramesh","year":"2021","unstructured":"Ramesh, K., Kumar, G.K., Swapna, K., Datta, D., Rajest, S.S.: A review of medical image segmentation algorithms. EAI Endorsed Trans. Pervasive Health Technol. 7(27), e6 (2021). https:\/\/doi.org\/10.4108\/eai.12-4-2021.169184","journal-title":"EAI Endorsed Trans. Pervasive Health Technol."},{"key":"25_CR16","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"},{"issue":"7","key":"25_CR17","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/83.701161","volume":"7","author":"S Sanjay-Gopal","year":"1998","unstructured":"Sanjay-Gopal, S., Hebert, T.J.: Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Trans. Image Process. 7(7), 1014\u20131028 (1998). https:\/\/doi.org\/10.1109\/83.701161","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR18","volume-title":"Stat. Anal. Finite Mixture Distrib.","author":"DM Titterington","year":"1985","unstructured":"Titterington, D.M., Smith, A.F., Makov, U.E.: Stat. Anal. Finite Mixture Distrib. Wiley series in probability and mathematical statistics, Whiley (1985)"},{"key":"25_CR19","doi-asserted-by":"publisher","unstructured":"Xia, X., Kulis, B.: W-Net: a deep model for fully unsupervised image segmentation. arXiv preprint arXiv:1711.08506 (2017). https:\/\/doi.org\/10.48550\/arXiv.1711.08506","DOI":"10.48550\/arXiv.1711.08506"},{"issue":"12","key":"25_CR20","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2018","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933\u20132946 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2018.2869576","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82484-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:05:12Z","timestamp":1741082712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82484-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031824838","9783031824845"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82484-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 March 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\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Castiglione della Pescaia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"21 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2024.icas.events\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}