{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:06:06Z","timestamp":1783969566631,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234974","type":"print"},{"value":"9789819234981","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3498-1_19","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:45:07Z","timestamp":1783968307000},"page":"218-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Feature Fusion Framework with Modal Collaboration for 3D PET\/CT Segmentation"],"prefix":"10.1007","author":[{"given":"Kerui","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"8","key":"19_CR1","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.21037\/qims-24-234","volume":"14","author":"T Li","year":"2024","unstructured":"Li, T., et al.: Fully automated classification of pulmonary nodules in positron emission tomography-computed tomography imaging using a two-stage multimodal learning approach. Quant. Imaging Med. Surg. 14(8), 5526 (2024)","journal-title":"Quant. Imaging Med. Surg."},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"102972","DOI":"10.1016\/j.media.2023.102972","volume":"90","author":"V Andrearczyk","year":"2023","unstructured":"Andrearczyk, V., et al.: Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET\/CT images: findings from the second edition of the HECKTOR challenge. Med. Image Anal. 90, 102972 (2023)","journal-title":"Med. Image Anal."},{"key":"19_CR3","series-title":"LNCS","first-page":"673","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Hong, Y.: A2FSeg: adaptive multi-modal fusion network for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention LNCS, vol. 14223, pp. 673\u2013681. Springer, Cham (2023)"},{"key":"19_CR4","series-title":"LNCS","first-page":"140","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Z Xing","year":"2022","unstructured":"Xing, Z., Yu, L., Wan, L., Han, T., Zhu, L.: NestedFormer: nested modality-aware transformer for brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention LNCS, vol. 13435, pp. 140\u2013150. Springer, Cham (2022)"},{"key":"19_CR5","first-page":"107","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., et al.: MMFormer: multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS, vol. 13435, pp. 107\u2013117. Springer, Cham (2022)"},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"H-Y Zhou","year":"2023","unstructured":"Zhou, H.-Y., et al.: nnFormer: Volumetric medical image segmentation via a 3D transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR7","series-title":"LNCS","first-page":"488","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"F Isensee","year":"2024","unstructured":"Isensee, F., Wald, T., Ulrich, C., 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 LNCS, vol. 15008, pp. 488\u2013498. Springer, Cham (2024)"},{"key":"19_CR8","series-title":"LNCS","first-page":"316","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"J Lu","year":"2024","unstructured":"Lu, J., Chen, J., Cai, L., et al.: H2ASeg: hierarchical adaptive interaction and weighting network for tumor segmentation in PET\/CT images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention LNCS, vol. 15008, pp. 316\u2013327. Springer, Cham (2024)"},{"issue":"7","key":"19_CR9","doi-asserted-by":"publisher","first-page":"995","DOI":"10.2967\/jnumed.123.267183","volume":"65","author":"LK Shiyam Sundar","year":"2024","unstructured":"Shiyam Sundar, L.K., Beyer, T.: Is automatic tumor segmentation on whole-body 18F-FDG PET images a clinical reality? J. Nucl. Med. 65(7), 995\u2013997 (2024)","journal-title":"J. Nucl. Med."},{"key":"19_CR10","series-title":"LNCS","first-page":"424","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016)"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/3DV.2016.79","volume-title":"2016 Fourth International Conference on 3D Vision (3DV)","author":"F Milletari","year":"2016","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE, Los Alamitos (2016)"},{"key":"19_CR12","series-title":"LNCS","first-page":"218","volume-title":"International MICCAI BrainLesion Workshop","author":"M Bhalerao","year":"2019","unstructured":"Bhalerao, M., Thakur, S.: Brain tumor segmentation based on 3D residual U-net. In: International MICCAI BrainLesion Workshop LNCS, vol. 11992, pp. 218\u2013225. Springer, Cham (2019)"},{"key":"19_CR13","series-title":"LNCS","first-page":"272","volume-title":"Interna-Tional MICCAI BrainLesion Workshop","author":"A Hatamizadeh","year":"2021","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: Interna-Tional MICCAI BrainLesion Workshop LNCS, vol. 12962, pp. 272\u2013284. Springer, Cham (2021)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: Transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584. IEEE, Los Alamitos (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"19_CR15","volume-title":"International Conference on Learning Representations (ICLR)","author":"A Dosovitskiy","year":"2021","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00d716 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021) OpenReview.net"},{"issue":"6","key":"19_CR16","doi-asserted-by":"publisher","first-page":"3928","DOI":"10.1007\/s10278-025-01481-y","volume":"38","author":"S Aburass","year":"2025","unstructured":"Aburass, S., Dorgham, O., Al Shaqsi, J., et al.: Vision transformers in medical imaging: a comprehensive review of advancements and applications across multiple diseases. J. Imaging Inform. Med. 38(6), 3928\u20133971 (2025)","journal-title":"J. Imaging Inform. Med."},{"issue":"12","key":"19_CR17","doi-asserted-by":"publisher","first-page":"3531","DOI":"10.1109\/TMI.2021.3089702","volume":"40","author":"Z Xue","year":"2021","unstructured":"Xue, Z., et al.: Multi-modal co-learning for liver lesion segmentation on PET-CT images. IEEE Trans. Med. Imaging. 40(12), 3531\u20133542 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","DOI":"10.1038\/s41597-022-01718-3"},{"issue":"2","key":"19_CR19","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.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":"19_CR20","doi-asserted-by":"crossref","unstructured":"Lan, L., Cai, P., Jiang, L., et al.: Brau-net++: U-shaped hybrid cnn-transformer network for medical image segmentation. IEEE Trans. Radiat. Plasma Med. Sci. (2026)","DOI":"10.1109\/TRPMS.2026.3666783"},{"key":"19_CR21","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.1109\/WACV61041.2025.00428","volume-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","author":"J Dhar","year":"2025","unstructured":"Dhar, J., Zaidi, N., Haghighat, M., et al.: Multimodal fusion learning with dual attention for medical imaging. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4362\u20134371. IEEE, Los Alamitos (2025)"},{"key":"19_CR22","first-page":"1","volume-title":"2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"J Zhou","year":"2025","unstructured":"Zhou, J., Sumi, C.: TRAM-UNet: transformer and region attention module based U-net for breast ultrasound image segmentation. In: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134. IEEE, Los Alamitos (2025)"},{"key":"19_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108784","volume":"178","author":"H Huang","year":"2024","unstructured":"Huang, H., Chen, Z., Zou, Y., et al.: Channel prior convolutional attention for medical image segmentation. Comput. Biol. Med. 178, 108784 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"19_CR24","doi-asserted-by":"publisher","first-page":"795","DOI":"10.2967\/jnumed.124.269067","volume":"66","author":"CS Constantino","year":"2025","unstructured":"Constantino, C.S., Oliveira, F.P.M., Machado, M., et al.: The use of maximum-intensity projections and deep learning adds value to the fully automatic segmentation of lesions avid for [18F]FDG and [68Ga]Ga-PSMA in PET\/CT. J. Nucl. Med. 66(5), 795\u2013801 (2025)","journal-title":"J. Nucl. Med."},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., et al.: SegMamba-v2: long-range sequential modeling mamba for general 3D medical image segmentation. IEEE Trans. Med. Imaging (2025)","DOI":"10.1109\/TMI.2025.3589797"},{"key":"19_CR26","first-page":"1","volume-title":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","author":"H Gong","year":"2025","unstructured":"Gong, H., Kang, L., Wang, Y., et al.: nnMamba: 3D biomedical image segmentation, classification and landmark detection with state space model. In: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE, Los Alamitos (2025)"},{"key":"19_CR27","series-title":"LNCS","first-page":"360","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"J Wang","year":"2024","unstructured":"Wang, J., Chen, J., Chen, D., et al.: LKM-UNet: large kernel vision mamba UNet for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention LNCS, vol. 15008, pp. 360\u2013370. Springer, Cham (2024)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3498-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:45:11Z","timestamp":1783968311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3498-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234974","9789819234981"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3498-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}