{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T20:13:22Z","timestamp":1788984802636,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030982522","type":"print"},{"value":"9783030982539","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\/978-3-030-98253-9_18","type":"book-chapter","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T19:02:30Z","timestamp":1647111750000},"page":"189-201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multimodal PET\/CT Tumour Segmentation and\u00a0Prediction of\u00a0Progression-Free Survival Using a\u00a0Full-Scale UNet with\u00a0Attention"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-6836","authenticated-orcid":false,"given":"Emmanuelle","family":"Bourigault","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6880-5687","authenticated-orcid":false,"given":"Daniel R.","family":"McGowan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4584-4453","authenticated-orcid":false,"given":"Abolfazl","family":"Mehranian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8432-2511","authenticated-orcid":false,"given":"Bart\u0142omiej W.","family":"Papie\u017c","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,13]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Oreiller, V., et al.: Head and neck tumor segmentation in PET\/CT: the HECKTOR challenge. Med. Image Anal. (2021). (under revision)","DOI":"10.1016\/j.media.2021.102336"},{"key":"18_CR2","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":"18_CR3","doi-asserted-by":"crossref","unstructured":"Huang, B., et al.: Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: a dual-center study. Contrast Media Molec. Imaging 2018, Article ID 8923028, 12 (2018)","DOI":"10.1155\/2018\/8923028"},{"key":"18_CR4","unstructured":"Andrearczyk, V., et al.: Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. In: Proceedings of the Third Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research, vol. 121, pp. 33\u201343 (2020)"},{"key":"18_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","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"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2015, pp. 3431\u20133440 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"18_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-67194-5_1","volume-title":"Head and Neck Tumor Segmentation","author":"V Andrearczyk","year":"2021","unstructured":"Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET\/CT. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds.) HECKTOR 2020. LNCS, vol. 12603, pp. 1\u201321. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67194-5_1"},{"key":"18_CR8","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"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4\u20138 May 2020, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Jadon, S.: A survey of loss functions for semantic segmentation. In: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2020, pp. 1\u20137 (2020)","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. arxiv 2017. arXiv preprint arXiv:1708.02002 (2002)","DOI":"10.1109\/ICCV.2017.324"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","volume":"26","author":"Y Boykov","year":"2004","unstructured":"Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision. IEEE TPAMI 26, 1124\u20131137 (2004)","journal-title":"IEEE TPAMI"},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2017). https:\/\/doi.org\/10.17863\/CAM.6936","DOI":"10.17863\/CAM.6936"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Baek, S., He, Y., Allen, B.G., et al.: Deep segmentation networks predict survival of non-small cell lung cancer. Sci. Rep. 9(1), 17286 (2019). Accessed 21 Nov 2019","DOI":"10.1038\/s41598-019-53461-2"},{"issue":"4","key":"18_CR15","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/MSP.2019.2900993","volume":"36","author":"P Afshar","year":"2019","unstructured":"Afshar, P., Mohammadi, A., Plataniotis, K.N., Oikonomou, A., Benali, H.: From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process. Mag. 36(4), 132\u2013160 (2019)","journal-title":"IEEE Signal Process. Mag."},{"key":"18_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. CoRR, vol. abs\/1709.01507 (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"18_CR18","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)"},{"issue":"10","key":"18_CR19","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.diii.2018.05.008","volume":"99","author":"H Akai","year":"2018","unstructured":"Akai, H., et al.: Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn. Interv. Imag. 99(10), 643\u2013651 (2018). Epub 2018 Jun 14 PMID: 29910166","journal-title":"Diagn. Interv. Imag."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Qiu, X., Gao, J., Yang, J., et al.: A comparison study of machine learning (random survival forest) and classic statistic (Cox proportional hazards) for predicting progression in high-grade glioma after proton and carbon ion radiotherapy. Front Oncol. 10, 551420 (2020). Accessed 30 Oct 2020","DOI":"10.3389\/fonc.2020.551420"},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., et al.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 24 (2018)","journal-title":"BMC Med. Res. Methodol."},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"6994","DOI":"10.1038\/s41598-019-43372-7","volume":"9","author":"DW Kim","year":"2019","unstructured":"Kim, D.W., Lee, S., Kwon, S., et al.: Deep learning-based survival prediction of oral cancer patients. Sci. Rep. 9, 6994 (2019)","journal-title":"Sci. Rep."},{"key":"#cr-split#-18_CR23.1","doi-asserted-by":"crossref","unstructured":"Kang, S.R., et al.: Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging, 13-14 January 2021. AACR","DOI":"10.1158\/1557-3265.ADI21-PO-029"},{"key":"#cr-split#-18_CR23.2","doi-asserted-by":"crossref","unstructured":"Clin. Cancer Res. 27(5 Suppl), Abstract nr PO-029 (2021)","DOI":"10.1158\/1557-3265.ADI21-PO-029"},{"key":"18_CR24","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1023\/A:1024068626366","volume":"52","author":"C Nadeau","year":"2003","unstructured":"Nadeau, C., Bengio, Y.: Inference for the generalization error. Mach. Learn. 52, 239\u2013281 (2003)","journal-title":"Mach. Learn."},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Abraham, N., Khan, N.: A novel focal tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683\u2013687 (2019)","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"18_CR26","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.compmedimag.2017.06.003","volume":"65","author":"P Swierczynski","year":"2018","unstructured":"Swierczynski, P., et al.: A level-set approach to joint image segmentation and registration with application to CT lung imaging. Comput. Med. Imaging Graph. 65, 58\u201368 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"key":"18_CR27","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.media.2016.03.002","volume":"32","author":"B Irving","year":"2016","unstructured":"Irving, B., et al.: Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation. Med. Image Anal. 32, 69\u201383 (2016)","journal-title":"Med. Image Anal."},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Zhong, Z., et al.: 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 228\u2013231 (2018)","DOI":"10.1109\/ISBI.2018.8363561"}],"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_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T23:12:59Z","timestamp":1674947579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98253-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030982522","9783030982539"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98253-9_18","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":"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"}}]}}