{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:09:04Z","timestamp":1764842944968,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031737473"},{"type":"electronic","value":"9783031737480"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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\/978-3-031-73748-0_1","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:02:33Z","timestamp":1729796553000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Real Time Multi Organ Classification on\u00a0Computed Tomography Images"],"prefix":"10.1007","author":[{"given":"Halid Ziya","family":"Yerebakan","sequence":"first","affiliation":[]},{"given":"Yoshihisa","family":"Shinagawa","sequence":"additional","affiliation":[]},{"given":"Gerardo Hermosillo","family":"Valadez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., et al.: Beyond self-attention: deformable large kernel attention for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1287\u20131297 (2024)","DOI":"10.1109\/WACV57701.2024.00132"},{"key":"1_CR2","unstructured":"Bai, X., Xia, Y.: SAM++: enhancing anatomic matching using semantic information and structural inference. arXiv preprint arXiv:2306.13988 (2023)"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Cao, H., et al. Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) European Conference on Computer Vision,vol. 13803, pp. 205\u2013218. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Agarwal, D., Aggarwal, K., Safta, W., Balan, M.M., Brown, K.: Masked image modeling advances 3D medical image analysis. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1970\u20131980 (2023)","DOI":"10.1109\/WACV56688.2023.00201"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Ghesu, F.C., Georgescu, B., Grbic, S., Maier, A.K., Hornegger, J., Comaniciu, D.: Robust multi-scale anatomical landmark detection in incomplete 3D-CT data. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 194\u2013202. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-662-56537-7_24","DOI":"10.1007\/978-3-662-56537-7_24"},{"issue":"8","key":"1_CR6","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"Gibson, E., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Med. Imaging 37(8), 1822\u20131834 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Goncharov, M., Soboleva, V., Kurmukov, A., Pisov, M., Belyaev, M.: vox2vec: a framework for self-supervised contrastive learning of voxel-level representations in medical images. In: Greenspan, H., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 605\u2013614. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_58","DOI":"10.1007\/978-3-031-43907-0_58"},{"key":"1_CR8","doi-asserted-by":"publisher","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: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes 2021. LNCS, vol. 12962, pp. 272\u2013284. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"1_CR9","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 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"1_CR10","unstructured":"Myronenko, A., Yang, D., He, Y., Xu, D.: Automated segmentation of organs and tumors from partially labeled 3D CT in MICCAI flare 2023 challenge (2023)"},{"key":"1_CR11","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"},{"key":"1_CR12","unstructured":"Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M.-H., Khan, F.S.: UNETR++: delving into efficient and accurate 3D medical image segmentation. arXiv preprint arXiv:2212.04497 (2022)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Tadokoro, R., Yamada, R., Kataoka, H.: Pre-training auto-generated volumetric shapes for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4739\u20134744 (2023)","DOI":"10.1109\/CVPRW59228.2023.00502"},{"key":"1_CR14","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)","DOI":"10.1148\/ryai.230024"},{"issue":"10","key":"1_CR15","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1109\/TMI.2022.3169003","volume":"41","author":"K Yan","year":"2022","unstructured":"Yan, K., et al.: SAM: self-supervised learning of pixel-wise anatomical embeddings in radiological images. IEEE Trans. Med. Imaging 41(10), 2658\u20132669 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"1_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., et al.: MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10(1), 41 (2023)","journal-title":"Sci. Data"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Yerebakan, H.Z., Shinagawa, Y., Ranganath, M., Allen-Raffl, S., Valadez, G.H.: A hierarchical descriptor framework for on-the-fly anatomical location matching between longitudinal studies. CoRR abs\/2308.07337 (2023)","DOI":"10.1007\/978-3-031-47425-5_6"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Rahman Siddiquee, Md.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018, Proceedings 4, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Data Engineering in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73748-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:03:56Z","timestamp":1729796636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73748-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031737473","9783031737480"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73748-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Data Engineering in Medical Imaging","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":"11 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"demi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/demi-workshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}