{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T04:15:08Z","timestamp":1771906508249,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164422","type":"print"},{"value":"9783031164439","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16443-9_53","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"549-559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Reinforcement Learning for Small Bowel Path Tracking Using Different Types of Annotations"],"prefix":"10.1007","author":[{"given":"Seung Yeon","family":"Shin","sequence":"first","affiliation":[]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"53_CR1","unstructured":"Small bowel obstruction (2019). https:\/\/my.clevelandclinic.org\/health\/diseases\/15850-small-bowel-obstruction"},{"key":"53_CR2","unstructured":"Chou, P.W., Maturana, D., Scherer, S.: Improving stochastic policy gradients in continuous control with deep reinforcement learning using the beta distribution. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 834\u2013843. PMLR, 06\u201311 August 2017. https:\/\/proceedings.mlr.press\/v70\/chou17a.html"},{"key":"53_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","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: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"53_CR4","unstructured":"Dai, T., et al.: Deep reinforcement learning for subpixel neural tracking. In: Cardoso, M.J., et al. (eds.) Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol. 102, pp. 130\u2013150. PMLR, 08\u201310 July 2019. https:\/\/proceedings.mlr.press\/v102\/dai19a.html"},{"issue":"9","key":"53_CR5","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012). https:\/\/doi.org\/10.1016\/j.mri.2012.05.001","journal-title":"Magn. Reson. Imaging"},{"key":"53_CR6","doi-asserted-by":"crossref","unstructured":"van Harten, L., de Jonge, C., Stoker, J., Isgum, I.: Untangling the small intestine in 3D cine-MRI using deep stochastic tracking. In: Medical Imaging with Deep Learning (2021). https:\/\/openreview.net\/forum?id=cfYAFR6s6iJ","DOI":"10.1016\/j.media.2022.102386"},{"key":"53_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"53_CR8","unstructured":"Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., M\u00fcller, K. (eds.) Advances in Neural Information Processing Systems, vol. 12. MIT Press (1999)"},{"key":"53_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/978-3-030-87240-3_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Z Li","year":"2021","unstructured":"Li, Z., Xia, Q., Hu, Z., Wang, W., Xu, L., Zhang, S.: A deep reinforced tree-traversal agent for coronary artery centerline extraction. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 418\u2013428. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_40"},{"issue":"2","key":"53_CR10","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1002\/cyto.a.20022","volume":"58A","author":"E Meijering","year":"2004","unstructured":"Meijering, E., Jacob, M., Sarria, J.C., Steiner, P., Hirling, H., Unser, M.: Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry Part A 58A(2), 167\u2013176 (2004). https:\/\/doi.org\/10.1002\/cyto.a.20022","journal-title":"Cytometry Part A"},{"key":"53_CR11","doi-asserted-by":"publisher","unstructured":"Oda, H., et al.: Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization. In: Mazurowski, M.A., Drukker, K. (eds.) Medical Imaging 2021: Computer-Aided Diagnosis. vol. 11597, pp. 388\u2013395. International Society for Optics and Photonics, SPIE (2021). https:\/\/doi.org\/10.1117\/12.2581261","DOI":"10.1117\/12.2581261"},{"key":"53_CR12","doi-asserted-by":"crossref","unstructured":"Pinto, L., Andrychowicz, M., Welinder, P., Zaremba, W., Abbeel, P.: Asymmetric actor critic for image-based robot learning. arXiv preprint arXiv:1710.06542 (2017)","DOI":"10.15607\/RSS.2018.XIV.008"},{"key":"53_CR13","unstructured":"Schulman, J., Moritz, P., Levine, S., Jordan, M.I., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016, Conference Track Proceedings (2016)"},{"key":"53_CR14","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"53_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-030-59719-1_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"SY Shin","year":"2020","unstructured":"Shin, S.Y., Lee, S., Elton, D., Gulley, J.L., Summers, R.M.: Deep small bowel segmentation with cylindrical topological constraints. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 207\u2013215. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_21"},{"key":"53_CR16","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-87199-4_27","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2021","author":"SY Shin","year":"2021","unstructured":"Shin, S.Y., Lee, S., Summers, R.M.: Unsupervised domain adaptation for small bowel segmentation using disentangled representation. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, pp. 282\u2013292. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_27"},{"key":"53_CR17","doi-asserted-by":"publisher","unstructured":"Shin, S.Y., Lee, S., Summers, R.M.: A graph-theoretic algorithm for small bowel path tracking in CT scans. In: Drukker, K., Iftekharuddin, K.M., Lu, H., Mazurowski, M.A., Muramatsu, C., Samala, R.K. (eds.) Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 863\u2013868. International Society for Optics and Photonics, SPIE (2022). https:\/\/doi.org\/10.1117\/12.2611878","DOI":"10.1117\/12.2611878"},{"key":"53_CR18","doi-asserted-by":"publisher","unstructured":"Shin, S.Y., Lee, S., Summers, R.M.: Graph-based small bowel path tracking with cylindrical constraints. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135 (2022). https:\/\/doi.org\/10.1109\/ISBI52829.2022.9761423","DOI":"10.1109\/ISBI52829.2022.9761423"},{"key":"53_CR19","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), September 2018","DOI":"10.1007\/978-3-030-01261-8_1"},{"issue":"2","key":"53_CR20","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.jcp.2005.08.005","volume":"212","author":"L Yatziv","year":"2006","unstructured":"Yatziv, L., Bartesaghi, A., Sapiro, G.: O(n) implementation of the fast marching algorithm. J. Comput. Phys. 212(2), 393\u2013399 (2006). https:\/\/doi.org\/10.1016\/j.jcp.2005.08.005","journal-title":"J. Comput. Phys."},{"key":"53_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/978-3-030-00937-3_86","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"P Zhang","year":"2018","unstructured":"Zhang, P., Wang, F., Zheng, Y.: Deep reinforcement learning for vessel centerline tracing in multi-modality 3D volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 755\u2013763. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_86"},{"issue":"11","key":"53_CR22","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1109\/TMI.2013.2271487","volume":"32","author":"W Zhang","year":"2013","unstructured":"Zhang, W., et al.: Mesenteric vasculature-guided small bowel segmentation on 3-D CT. IEEE Trans. Med. Imaging 32(11), 2006\u20132021 (2013). https:\/\/doi.org\/10.1109\/TMI.2013.2271487","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16443-9_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:48:57Z","timestamp":1709830137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}