{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:24:48Z","timestamp":1758450288600,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049469"},{"type":"electronic","value":"9783032049476"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04947-6_54","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:33:01Z","timestamp":1758389581000},"page":"566-576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self is the\u00a0Best Learner: CT-Free Ultra-low-Dose PET Organ Segmentation via\u00a0Collaborating Denoising and\u00a0Segmentation Learning"],"prefix":"10.1007","author":[{"given":"Zanting","family":"Ye","sequence":"first","affiliation":[]},{"given":"Xiaolong","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Han","sequence":"additional","affiliation":[]},{"given":"Xuanbin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Wantong","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yijun","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yanchao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hubing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"issue":"3","key":"54_CR1","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1148\/radiology.219.3.r01ma08774","volume":"219","author":"J Aoki","year":"2001","unstructured":"Aoki, J., et al.: Fdg pet of primary benign and malignant bone tumors: standardized uptake value in 52 lesions. Radiology 219(3), 774\u2013777 (2001)","journal-title":"Radiology"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Bao, N., et al.: Ct-less whole-body bone segmentation of pet images using a multimodal deep learning network. IEEE J. Biomed. Health Inf. (2024)","DOI":"10.1109\/JBHI.2024.3501386"},{"key":"54_CR3","doi-asserted-by":"crossref","unstructured":"Chang, A., Zeng, J., Huang, R., Ni, D.: Em-net: efficient channel and frequency learning with mamba for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 266\u2013275. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_26"},{"issue":"12","key":"54_CR4","doi-asserted-by":"publisher","first-page":"4145","DOI":"10.1007\/s00259-022-05893-8","volume":"49","author":"W Chen","year":"2022","unstructured":"Chen, W., et al.: Evaluation of pediatric malignancies using total-body pet\/ct with half-dose [18f]-fdg. Eur. J. Nucl. Med. Mol. Imaging 49(12), 4145\u20134155 (2022)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"54_CR5","doi-asserted-by":"publisher","first-page":"103046","DOI":"10.1016\/j.media.2023.103046","volume":"92","author":"S Dayarathna","year":"2024","unstructured":"Dayarathna, S., Islam, K.T., Uribe, S., Yang, G., Hayat, M., Chen, Z.: Deep learning based synthesis of mri, ct and pet: review and analysis. Med. Image Anal. 92, 103046 (2024)","journal-title":"Med. Image Anal."},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"54_CR7","unstructured":"Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"issue":"2","key":"54_CR9","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. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"54_CR10","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnu-net revisited: a call for rigorous validation in 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 488\u2013498. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"issue":"10","key":"54_CR11","doi-asserted-by":"publisher","first-page":"2974","DOI":"10.1109\/TMI.2023.3273029","volume":"42","author":"C Jiang","year":"2023","unstructured":"Jiang, C., Pan, Y., Cui, Z., Nie, D., Shen, D.: Semi-supervised standard-dose pet image generation via region-adaptive normalization and structural consistency constraint. IEEE Trans. Med. Imaging 42(10), 2974\u20132987 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"54_CR12","doi-asserted-by":"publisher","first-page":"19805","DOI":"10.1038\/s41598-023-46747-z","volume":"13","author":"B Kovacs","year":"2023","unstructured":"Kovacs, B., et al.: Addressing image misalignments in multi-parametric prostate mri for enhanced computer-aided diagnosis of prostate cancer. Sci. Rep. 13(1), 19805 (2023)","journal-title":"Sci. Rep."},{"key":"54_CR13","unstructured":"Li, W., Yuille, A., Zhou, Z.: How well do supervised models transfer to 3d image segmentation. In: The Twelfth International Conference on Learning Representations, vol.\u00a01 (2024)"},{"issue":"8","key":"54_CR14","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1007\/s00259-021-05500-2","volume":"49","author":"G Liu","year":"2022","unstructured":"Liu, G., et al.: Short-time total-body dynamic pet imaging performance in quantifying the kinetic metrics of 18f-fdg in healthy volunteers. Eur. J. Nucl. Med. Mol. Imaging 49(8), 2493\u20132503 (2022)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"1","key":"54_CR15","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13014-024-02409-6","volume":"19","author":"Z Mansouri","year":"2024","unstructured":"Mansouri, Z., et al.: Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and ct radiomics features: a multicentric study. Radiat. Oncol. 19(1), 12 (2024)","journal-title":"Radiat. Oncol."},{"key":"54_CR16","unstructured":"Munk, A., Ambsdorf, J., Llambias, S., Nielsen, M.: Amaes: augmented masked autoencoder pretraining on public brain mri data for 3d-native segmentation. arXiv preprint arXiv:2408.00640 (2024)"},{"issue":"4","key":"54_CR17","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/s10278-020-00321-5","volume":"33","author":"KT Oh","year":"2020","unstructured":"Oh, K.T., Lee, S., Lee, H., Yun, M., Yoo, S.K.: Semantic segmentation of white matter in fdg-pet using generative adversarial network. J. Digit. Imaging 33(4), 816\u2013825 (2020)","journal-title":"J. Digit. Imaging"},{"key":"54_CR18","unstructured":"Rokuss, M., et al.: From fdg to psma: a hitchhiker\u2019s guide to multitracer, multicenter lesion segmentation in pet\/ct imaging. arXiv preprint arXiv:2409.09478 (2024)"},{"key":"54_CR19","doi-asserted-by":"publisher","DOI":"10.1097\/RLU.0000000000005685","author":"Y Salimi","year":"2025","unstructured":"Salimi, Y., Mansouri, Z., Shiri, I., Mainta, I., Zaidi, H.: Deep learning-powered ct-less multitracer organ segmentation from pet images: a solution for unreliable ct segmentation in pet\/ct imaging. Clin. Nucl. Med. (2025). https:\/\/doi.org\/10.1097\/RLU.0000000000005685","journal-title":"Clin. Nucl. Med."},{"key":"54_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","volume":"75","author":"SA Taghanaki","year":"2019","unstructured":"Taghanaki, S.A., et al.: Combo loss: Handling input and output imbalance in multi-organ segmentation. Comput. Med. Imaging Graph. 75, 24\u201333 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"key":"54_CR21","doi-asserted-by":"crossref","unstructured":"Wald, T., et al.: Revisiting mae pre-training for 3d medical image segmentation. arXiv preprint arXiv:2410.23132 (2024)","DOI":"10.1109\/CVPR52734.2025.00489"},{"key":"54_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., et\u00a0al.: Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body pet imaging: a multi-center and cross-tracer study. Eur. J. Nuclear Med. Molecular Imaging 1\u201315 (2025)","DOI":"10.1007\/s00259-025-07156-8"},{"issue":"1081","key":"54_CR23","doi-asserted-by":"publisher","first-page":"20170508","DOI":"10.1259\/bjr.20170508","volume":"91","author":"H Zaidi","year":"2017","unstructured":"Zaidi, H., Karakatsanis, N.: Towards enhanced pet quantification in clinical oncology. Br. J. Radiol. 91(1081), 20170508 (2017)","journal-title":"Br. J. Radiol."},{"key":"54_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cui, Z., Jiang, C., Guo, S., Gao, F., Shen, D.: Hierarchical organ-aware total-body standard-dose pet reconstruction from low-dose pet and ct images. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3266551"},{"issue":"11","key":"54_CR25","doi-asserted-by":"publisher","first-page":"3154","DOI":"10.1109\/TMI.2021.3076191","volume":"40","author":"B Zhou","year":"2021","unstructured":"Zhou, B., Tsai, Y.J., Chen, X., Duncan, J.S., Liu, C.: Mdpet: a unified motion correction and denoising adversarial network for low-dose gated pet. IEEE Trans. Med. Imaging 40(11), 3154\u20133164 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"54_CR26","doi-asserted-by":"publisher","first-page":"101770","DOI":"10.1016\/j.media.2020.101770","volume":"65","author":"L Zhou","year":"2020","unstructured":"Zhou, L., Schaefferkoetter, J.D., Tham, I.W., Huang, G., Yan, J.: Supervised learning with cyclegan for low-dose fdg pet image denoising. Med. Image Anal. 65, 101770 (2020)","journal-title":"Med. Image Anal."},{"issue":"2","key":"54_CR27","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1148\/rg.2016150102","volume":"36","author":"P Ziai","year":"2016","unstructured":"Ziai, P., et al.: Role of optimal quantification of fdg pet imaging in the clinical practice of radiology. Radiographics 36(2), 481\u2013496 (2016)","journal-title":"Radiographics"}],"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\/978-3-032-04947-6_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:33:10Z","timestamp":1758389590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 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 are relevant to the content of this article.","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"}}]}}