{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T10:45:30Z","timestamp":1747392330718,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031730825"},{"type":"electronic","value":"9783031730832"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-73083-2_1","type":"book-chapter","created":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T23:02:44Z","timestamp":1727564564000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CloverNet \u2013 Leveraging Planning Annotations for\u00a0Enhanced Procedural MR Segmentation: An\u00a0Application to\u00a0Adaptive Radiation Therapy"],"prefix":"10.1007","author":[{"given":"Francesca","family":"De Benetti","sequence":"first","affiliation":[]},{"given":"Yousef","family":"Yaganeh","sequence":"additional","affiliation":[]},{"given":"Claus","family":"Belka","sequence":"additional","affiliation":[]},{"given":"Stefanie","family":"Corradini","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Kurz","sequence":"additional","affiliation":[]},{"given":"Guillaume","family":"Landry","sequence":"additional","affiliation":[]},{"given":"Shadi","family":"Albarqouni","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Wendler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"unstructured":"D\u2019Antonoli, T.A., et\u00a0al.: TotalSegmentator MRI: sequence-independent segmentation of 59 anatomical structures in MR images. arXiv preprints arXiv:2405.19492 (2024)","key":"1_CR1"},{"doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, 2016, Proceedings, Part II 19 (2016)","key":"1_CR2","DOI":"10.1007\/978-3-319-46723-8_49"},{"doi-asserted-by":"crossref","unstructured":"Elmahdy, M.S., et al.: Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer. IEEE Access 9, 95551\u201395568 (2021)","key":"1_CR3","DOI":"10.1109\/ACCESS.2021.3091011"},{"doi-asserted-by":"crossref","unstructured":"Eppenhof, K.A., et al.: Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks. Med. Phys. 47(3), 1238\u201348 (2020)","key":"1_CR4","DOI":"10.1002\/mp.13994"},{"doi-asserted-by":"crossref","unstructured":"Hemon, C., et al.: Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer. J. Appl. Clin. Med. Phys. 24(8), e13991 (2023)","key":"1_CR5","DOI":"10.1002\/acm2.13991"},{"doi-asserted-by":"crossref","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. Methods 18(2), 203\u201311 (2021)","key":"1_CR6","DOI":"10.1038\/s41592-020-01008-z"},{"doi-asserted-by":"crossref","unstructured":"Kawula, M., et al.: Prior knowledge based deep learning auto-segmentation in magnetic resonance imaging-guided radiotherapy of prostate cancer. Phys. Imag. Radiat. Oncol. 28, 100498(2023)","key":"1_CR7","DOI":"10.1016\/j.phro.2023.100498"},{"doi-asserted-by":"crossref","unstructured":"Khor, H.G., Ning, G., Sun, Y., Lu, X., Zhang, X., Liao, H.: Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration. Med. Image Anal. 88, 102811 (2023)","key":"1_CR8","DOI":"10.1016\/j.media.2023.102811"},{"doi-asserted-by":"crossref","unstructured":"Kolenbrander, I.D., et al.: Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy. Med. Phys. 51(4), 2367\u201377 (2024)","key":"1_CR9","DOI":"10.1002\/mp.17000"},{"doi-asserted-by":"crossref","unstructured":"Landry, G., Kurz, C., Traverso, A.: The role of artificial intelligence in radiotherapy clinical practice. BJR Open 5(1), 20230030 (2023)","key":"1_CR10","DOI":"10.1259\/bjro.20230030"},{"doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy. Radiother. Oncol. 177, 222\u2013230 (2022)","key":"1_CR11","DOI":"10.1016\/j.radonc.2022.11.004"},{"doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)","key":"1_CR12","DOI":"10.1109\/CVPR.2016.433"},{"doi-asserted-by":"crossref","unstructured":"Ng, J., et al.: MRI-LINAC: a transformative technology in radiation oncology. Front. Oncol. 13, 1117874 (2023)","key":"1_CR13","DOI":"10.3389\/fonc.2023.1117874"},{"doi-asserted-by":"crossref","unstructured":"Shepherd, M., et al.: A scoping review of advanced practice in online adaptive radiotherapy: educational needs and training for evidence and opportunity building. J. Med. Imag. Radiat. Sci. 54(4), S6 (2023)","key":"1_CR14","DOI":"10.1016\/j.jmir.2023.09.018"},{"doi-asserted-by":"crossref","unstructured":"Wasserthal, J., et\u00a0al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5) (2023)","key":"1_CR15","DOI":"10.1148\/ryai.230024"},{"doi-asserted-by":"publisher","unstructured":"Zhou, Z., et al.: macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND. Biomed. Eng. Online 22(1),(2023). https:\/\/doi.org\/10.1186\/s12938-023-01143-6","key":"1_CR16","DOI":"10.1186\/s12938-023-01143-6"}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73083-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T23:03:08Z","timestamp":1727564588000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73083-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031730825","9783031730832"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73083-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Clinical Image-Based Procedures","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":"5 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"clip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}