{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T16:00:37Z","timestamp":1726156837217},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030982522"},{"type":"electronic","value":"9783030982539"}],"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\/978-3-030-98253-9_22","type":"book-chapter","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T14:02:30Z","timestamp":1647093750000},"page":"236-247","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Segmentation and Risk Score Prediction of Head and Neck Cancers in PET\/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5261-6163","authenticated-orcid":false,"given":"Fereshteh","family":"Yousefirizi","sequence":"first","affiliation":[]},{"given":"Ian","family":"Janzen","sequence":"additional","affiliation":[]},{"given":"Natalia","family":"Dubljevic","sequence":"additional","affiliation":[]},{"given":"Yueh-En","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chloe","family":"Hill","sequence":"additional","affiliation":[]},{"given":"Calum","family":"MacAulay","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9980-2403","authenticated-orcid":false,"given":"Arman","family":"Rahmim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,13]]},"reference":[{"issue":"12","key":"22_CR1","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/j.oraloncology.2012.06.019","volume":"48","author":"M O\u2019rorke","year":"2012","unstructured":"O\u2019rorke, M., Ellison, M., Murray, L., et al.: Human papillomavirus related head and neck cancer survival: a systematic review and meta-analysis. Oral Oncol. 48(12), 1191\u20131201 (2012)","journal-title":"Oral Oncol."},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Starmans, M.P., van der Voort, S.R., Tovar, J.M.C., et al.: Radiomics: data mining using quantitative medical image features. In: Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 429\u2013456. Elsevier (2020).","DOI":"10.1016\/B978-0-12-816176-0.00023-5"},{"key":"22_CR3","doi-asserted-by":"publisher","unstructured":"Jin, D., et al.: Accurate esophageal gross tumor volume segmentation in PET\/CT using two-stream chained 3D deep network fusion. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 182\u2013191. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_21","DOI":"10.1007\/978-3-030-32245-8_21"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"202553","DOI":"10.1148\/radiol.2021202553","volume":"298","author":"MR Tomaszewski","year":"2021","unstructured":"Tomaszewski, M.R., Gillies, R.J.: The biological meaning of radiomic features. Radiology 298, 202553 (2021)","journal-title":"Radiology"},{"key":"22_CR5","unstructured":"Kvamme, H., Borgan, \u00d8., Scheel, I.: Time-to-event prediction with neural networks and Cox regression. arXiv preprint arXiv:1907.00825 (2019)"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Oreiller, V., Andrearczyk, V.: Head and Neck Tumor Segmentation in PET\/CT: The HECKTOR Challenge. Medical Image Analysis (2021). Under revision","DOI":"10.1007\/978-3-030-67194-5"},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-030-98253-9","volume-title":"Head and Neck Tumor Segmentation and Outcome Prediction","author":"V Andrearczyk","year":"2022","unstructured":"Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET\/CT images. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds.) HECKTOR 2021. LNCS, vol. 13209, pp. 1\u201337. Springer, Cham (2022)"},{"key":"22_CR8","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":"22_CR9","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., et al.: NNU-net: self-adapting framework for U-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"22_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-030-67194-5_4","volume-title":"Head and Neck Tumor Segmentation","author":"A Iantsen","year":"2021","unstructured":"Iantsen, A., Visvikis, D., Hatt, M.: Squeeze-and-excitation normalization for automated delineation of head and neck primary tumors in combined PET and CT images. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds.) HECKTOR 2020. LNCS, vol. 12603, pp. 37\u201343. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67194-5_4"},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","volume":"38","author":"AG Roy","year":"2018","unstructured":"Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel \u201csqueeze and excitation\u201d blocks. IEEE Trans. Med. Imaging 38(2), 540\u2013549 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Yeung, M., Sala, E., Sch\u00f6nlieb, C.-B., et al.: Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. arXiv preprint arXiv:2102.04525 (2021)","DOI":"10.1016\/j.compmedimag.2021.102026"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TIP.2019.2941265","volume":"29","author":"B Kim","year":"2019","unstructured":"Kim, B., Ye, J.C.: Mumford-Shah loss functional for image segmentation with deep learning. IEEE Trans. Image Process. 29, 1856\u20131866 (2019)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"22_CR16","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/mp.13300","volume":"46","author":"W Zhu","year":"2019","unstructured":"Zhu, W., Huang, Y., Zeng, L., et al.: AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576\u2013589 (2019)","journal-title":"Med. Phys."},{"key":"22_CR17","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., Zheng, Y., Zhou, S.K., 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."},{"issue":"21","key":"22_CR18","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J.J., Fedorov, A., Parmar, C., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104\u2013e107 (2017)","journal-title":"Can. Res."},{"key":"22_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"22_CR20","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR21","first-page":"35","volume":"11","author":"B Langholz","year":"1996","unstructured":"Langholz, B., Goldstein, L.: Risk set sampling in epidemiologic cohort studies. Statist. Sci. 11, 35\u201353 (1996)","journal-title":"Statist. Sci."},{"key":"22_CR22","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., Gross, S., Massa, F., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Head and Neck Tumor Segmentation and Outcome Prediction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98253-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T14:06:01Z","timestamp":1647093961000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98253-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030982522","9783030982539"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98253-9_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"13 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HECKTOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3D Head and Neck Tumor Segmentation in PET\/CT Challenge","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","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":"hecktor2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aicrowd.com\/challenges\/miccai-2021-hecktor","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}