{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T22:10:42Z","timestamp":1785276642497,"version":"3.55.0"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031962011","type":"print"},{"value":"9783031962028","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"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\/978-3-031-96202-8_15","type":"book-chapter","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:29:19Z","timestamp":1752139759000},"page":"178-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DRL-STNet: Unsupervised Domain Adaptation for\u00a0Cross-Modality Medical Image Segmentation via\u00a0Disentangled Representation Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6559-2751","authenticated-orcid":false,"given":"Hui","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3959-5163","authenticated-orcid":false,"given":"Florian","family":"Schiffers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-7446","authenticated-orcid":false,"given":"Santiago","family":"L\u00f3pez-Tapia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1541-5917","authenticated-orcid":false,"given":"Neda","family":"Tavakoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2660-8973","authenticated-orcid":false,"given":"Daniel","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-0070","authenticated-orcid":false,"given":"Aggelos K.","family":"Katsaggelos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"issue":"8","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"102680","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","volume":"39","author":"C Chen","year":"2020","unstructured":"Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans. Med. Imaging 39, 2494\u20132505 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.2972701","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"9","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012)","journal-title":"Magn. Reson. Imaging"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Gatidis, S., et\u00a0al.: The autoPET challenge: towards fully automated lesion segmentation in oncologic PET\/CT imaging. preprint at Research Square (Nature Portfolio) (2023). https:\/\/doi.org\/10.21203\/rs.3.rs-2572595\/v1","DOI":"10.21203\/rs.3.rs-2572595\/v1"},{"issue":"1","key":"15_CR7","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","journal-title":"Sci. Data"},{"key":"15_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"101821","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge. Med. Image Anal. 67, 101821 (2021)","journal-title":"Med. Image Anal."},{"issue":"6","key":"15_CR11","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1200\/JCO.2020.38.6_suppl.626","volume":"38","author":"N Heller","year":"2020","unstructured":"Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626 (2020)","journal-title":"Proc. Am. Soc. Clin. Oncol."},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: Revisiting nnU-Net for iterative pseudo labeling and efficient sliding window inference. In: MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, pp. 178\u2013189. Springer (2022)","DOI":"10.1007\/978-3-031-23911-3_16"},{"issue":"2","key":"15_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18(2), 203\u2013211 (2021)","journal-title":"Nat. Meth."},{"key":"15_CR14","first-page":"36722","volume":"35","author":"Y Ji","year":"2022","unstructured":"Ji, Y., et al.: AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Adv. Neural. Inf. Process. Syst. 35, 36722\u201336732 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Jiang, K., Quan, L., Gong, T.: Disentangled representation and cross-modality image translation based unsupervised domain adaptation method for abdominal organ segmentation. Int. J. Comput. Assist. Radiol. Surg., 1\u201313 (2022). https:\/\/doi.org\/10.1007\/s11548-022-02590-7","DOI":"10.1007\/s11548-022-02590-7"},{"key":"15_CR16","unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol.\u00a03, p.\u00a0896. Atlanta (2013)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 35\u201351 (2018)","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Lin, H., Apostolidis, C., Katsaggelos, A.K.: Brighteye: glaucoma screening with color fundus photographs based on vision transformer. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134 (2024). https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635883","DOI":"10.1109\/ISBI56570.2024.10635883"},{"key":"15_CR19","unstructured":"Lin, H., Liu, T., Katsaggelos, A., Kline, A.: StenUNet: automatic stenosis detection from X-ray coronary angiography. arXiv preprint arXiv:2310.14961 (2023)"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Lin, H., et\u00a0al.: Usformer: a small network for left atrium segmentation of 3D LGE MRI. Heliyon (2024)","DOI":"10.1016\/j.heliyon.2024.e28539"},{"issue":"3","key":"15_CR21","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","volume":"13","author":"X Liu","year":"2021","unstructured":"Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability 13(3), 1224 (2021)","journal-title":"Sustainability"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Lou, M., Ying, H., Liu, X., Zhou, H.Y., Zhang, Y., Yu, Y.: SDR-former: a Siamese dual-resolution transformer for liver lesion classification using 3D multi-phase imaging. arXiv preprint arXiv:2402.17246 (2024)","DOI":"10.1016\/j.neunet.2025.107228"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15, 654 (2024)","journal-title":"Nat. Commun."},{"key":"15_CR24","unstructured":"Ma, J., et al.: Segment anything in medical images and videos: benchmark and deployment. arXiv preprint arXiv:2408.03322 (2024)"},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"102616","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)","journal-title":"Med. Image Anal."},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the FLARE22 challenge. Lancet Digital Health (2024)","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Automatic organ and pan-cancer segmentation in abdomen CT: the flare 2023 challenge. arXiv preprint arXiv:2408.12534 (2024)","DOI":"10.1007\/978-3-031-58776-4"},{"issue":"10","key":"15_CR28","doi-asserted-by":"publisher","first-page":"6695","DOI":"10.1109\/TPAMI.2021.3100536","volume":"44","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: AbdomenCT-1K: is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695\u20136714 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul\u00a0Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Schiffers, F., Yu, Z., Arguin, S., Maier, A., Ren, Q.: Synthetic fundus fluorescein angiography using deep neural networks. In: Bildverarbeitung f\u00fcr die Medizin 2018: Algorithmen-Systeme-Anwendungen. Proceedings des Workshops vom 11. bis 13. M\u00e4rz 2018 in Erlangen, pp. 234\u2013238. Springer (2018)","DOI":"10.1007\/978-3-662-56537-7_64"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Shi, Y., Zhu, F., Peng, Y., Ye, Z., Zhou, C.: A multi-task unsupervised domain adaptation network for medical image segmentation. In: International Conference on Image Processing and Intelligent Control (IPIC 2021), vol. 11928, pp. 65\u201370. SPIE (2021)","DOI":"10.1117\/12.2611637"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Shin, H., Kim, H., Kim, S., Jun, Y., Eo, T., Hwang, D.: SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7412\u20137421 (2023)","DOI":"10.1109\/CVPR52729.2023.00716"},{"key":"15_CR33","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"key":"15_CR34","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Wang, E., Zhao, Y., Wu, Y.: Cascade dual-decoders network for abdominal organs segmentation. In: MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, pp. 202\u2013213. Springer (2022)","DOI":"10.1007\/978-3-031-23911-3_18"},{"issue":"5","key":"15_CR36","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J., et al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023)","journal-title":"Radiol. Artif. Intell."},{"key":"15_CR37","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.1109\/TMI.2021.3090412","volume":"40","author":"F Wu","year":"2021","unstructured":"Wu, F., Zhuang, X.: Unsupervised domain adaptation with variational approximation for cardiac segmentation. IEEE Trans. Med. Imaging 40, 3555\u20133567 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3090412","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR38","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TMI.2022.3192303","volume":"43","author":"Q Xie","year":"2022","unstructured":"Xie, Q., et al.: Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training. IEEE Trans. Med. Imaging 43, 4\u201314 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3192303","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"15_CR39","doi-asserted-by":"publisher","first-page":"100543","DOI":"10.1016\/j.patter.2022.100543","volume":"3","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Codabench: flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3(7), 100543 (2022)","journal-title":"Patterns"},{"key":"15_CR40","doi-asserted-by":"publisher","first-page":"4976","DOI":"10.1109\/JBHI.2022.3162118","volume":"26","author":"K Yao","year":"2022","unstructured":"Yao, K., et al.: A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE J. Biomed. Health Inform. 26, 4976\u20134986 (2022). https:\/\/doi.org\/10.1109\/JBHI.2022.3162118","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"15_CR41","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., Gerig, G.: ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3342\u20133345 (2016)","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"15_CR42","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"}],"container-title":["Lecture Notes in Computer Science","Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96202-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:37:59Z","timestamp":1757209079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96202-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"ISBN":["9783031962011","9783031962028"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96202-8_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"9 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FLARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"flare2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.codabench.org\/competitions\/2319\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}