{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T06:05:39Z","timestamp":1779861939326,"version":"3.53.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031730825","type":"print"},{"value":"9783031730832","type":"electronic"}],"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_4","type":"book-chapter","created":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T19:02:44Z","timestamp":1727550164000},"page":"32-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HTSeg: Hybrid Two-Stage Segmentation Framework for\u00a0Intestine Segmentation from\u00a0CT Volumes"],"prefix":"10.1007","author":[{"given":"Qin","family":"An","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirohisa","family":"Oda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichiro","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayuki","family":"Kitasaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aitaro","family":"Takimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akinari","family":"Hinoki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroo","family":"Uchida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kojiro","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kesaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"4_CR1","unstructured":"Smith\u00a0DA, Kashyap\u00a0S, N.S.: Bowel obstruction. StatPearls (1 Aug 2022)"},{"key":"4_CR2","unstructured":"Sinicrope, F.: Ileus and Bowel Obstruction. Holland-Frei Cancer Medicine. 6th edition. Hamilton BC Decker (2003)"},{"issue":"5","key":"4_CR3","first-page":"945","volume":"98","author":"KL Bower","year":"2018","unstructured":"Bower, K.L., Lollar, D.I., Williams, S.L., Adkins, F.C., Luyimbazi, D.T., Bower, C.E.: Small bowel obstruction. Surg. Clin. 98(5), 945\u2013971 (2018)","journal-title":"Surg. Clin."},{"key":"4_CR4","unstructured":"Bogusevicius, A., Pundzius, J., Maleckas, A., Vilkauskas, L.: Computer-aided diagnosis of the character of bowel obstruction. Int. Surg. 84(3), 225\u2013228 (1999). http:\/\/europepmc.org\/abstract\/MED\/10533781"},{"issue":"2","key":"4_CR5","first-page":"63","volume":"36","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., et al.: Deep learning and its application to medical image segmentation. Med. Imaging Technol. 36(2), 63\u201371 (2018)","journal-title":"Med. Imaging Technol."},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: 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, September 20, 2018, Proceedings 4, pp. 3\u201311. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"27","key":"4_CR7","doi-asserted-by":"publisher","first-page":"e6","DOI":"10.4108\/eai.12-4-2021.169184","volume":"7","author":"K Ramesh","year":"2021","unstructured":"Ramesh, K., Kumar, G.K., Swapna, K., Datta, D., Rajest, S.S.: A review of medical image segmentation algorithms. EAI Endorsed Trans. Pervasive Health Technol. 7(27), e6\u2013e6 (2021)","journal-title":"EAI Endorsed Trans. Pervasive Health Technol."},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10278-020-00410-5","volume":"34","author":"Y Zeng","year":"2021","unstructured":"Zeng, Y., Tsui, P.H., Wu, W., Zhou, Z., Wu, S.: Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net. J. Digit. Imaging 34(1), 134\u2013148 (2021)","journal-title":"J. Digit. Imaging"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, LNCS 9351. pp. 234\u2013241. Springer (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-Unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331. IEEE (2018)","DOI":"10.1109\/ITME.2018.00080"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-Unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant. Imaging Med. Surg. 10(6), 1275 (2020)","DOI":"10.21037\/qims-19-1090"},{"key":"4_CR12","doi-asserted-by":"publisher","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 9901, pp. 424\u2013432. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, W., Kim, H.M.: Fully automatic colon segmentation in computed tomography colonography. In: 2016 IEEE International Conference on Signal and Image Processing (ICSIP), pp. 51\u201355. IEEE (2016)","DOI":"10.1109\/SIPROCESS.2016.7888222"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Barr, K., Laframboise, J., Ungi, T., Hookey, L., Fichtinger, G.: Automated segmentation of computed tomography colonography images using a 3D U-Net. In: SPIE Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11315, pp. 635\u2013641 (2020)","DOI":"10.1117\/12.2549749"},{"issue":"4","key":"4_CR15","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.compmedimag.2009.02.004","volume":"33","author":"A Bert","year":"2009","unstructured":"Bert, A., et al.: An automatic method for colon segmentation in CT colonography. Comput. Med. Imaging Graph. 33(4), 325\u2013331 (2009). https:\/\/doi.org\/10.1016\/j.compmedimag.2009.02.004","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"4_CR16","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/2945.856997","volume":"6","author":"Y Sato","year":"2000","unstructured":"Sato, Y., et al.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans. Visual Comput. Graphics 6(2), 160\u2013180 (2000)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"issue":"8","key":"4_CR17","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1118\/1.1990288","volume":"32","author":"H Frimmel","year":"2005","unstructured":"Frimmel, H., N\u00e4ppi, J., Yoshida, H.: Centerline-based colon segmentation for CT colonography. Med. Phys. 32(8), 2665\u20132672 (2005)","journal-title":"Med. Phys."},{"issue":"1","key":"4_CR18","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10044-017-0614-y","volume":"21","author":"K Rajamani","year":"2018","unstructured":"Rajamani, K., et al.: Segmentation of colon and removal of opacified fluid for virtual colonoscopy. Pattern Anal. Appl. 21(1), 205\u2013219 (2018)","journal-title":"Pattern Anal. Appl."},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision, pp. 205\u2013218. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"4_CR20","unstructured":"Zou, Y., Zhang, Z., Zhang, H., Li, C.L., Bian, X., Huang, J.B., Pfister, T.: Pseudoseg: Designing pseudo labels for semantic segmentation. arXiv preprint arXiv:2010.09713 (2020)"},{"key":"4_CR21","unstructured":"Qin, A., et al.: Intestine Segmentation from CT Volume based on Bidirectional Teaching. In: SPIE Medical Imaging 2024: Image Processing (accepted), vol. 12926, pp. 238\u2013243 (2024)"},{"issue":"12","key":"4_CR22","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-Denseunet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"4_CR25","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Proc. Syst. 30 (2017)"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T05:38:55Z","timestamp":1779860335000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73083-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031730825","9783031730832"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73083-2_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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"}}]}}