{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:13:38Z","timestamp":1783725218445,"version":"3.55.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032241818","type":"print"},{"value":"9783032241825","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-24182-5_11","type":"book-chapter","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:22:50Z","timestamp":1783722170000},"page":"114-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["XBoundNet++: Uncertainty-Aware Segmentation of Kidney Ablation Zones"],"prefix":"10.1007","author":[{"given":"Oren","family":"Arbel-Wood","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Rastegarpoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Fenster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"11_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-32245-8_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"CF Baumgartner","year":"2019","unstructured":"Baumgartner, C.F., et al.: PHISeg: capturing uncertainty in medical image segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 119\u2013127. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_14"},{"key":"11_CR2","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":"11_CR3","doi-asserted-by":"publisher","first-page":"103623","DOI":"10.1016\/j.media.2025.103623","volume":"85","author":"S Chatterjee","year":"2025","unstructured":"Chatterjee, S., Honchar, A., Seibold, M., et al.: PULASki: learning inter-rater variability using statistical distances to improve probabilistic segmentation. Med. Image Anal. 85, 103623 (2025)","journal-title":"Med. Image Anal."},{"issue":"4","key":"11_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10441-6","author":"S Das","year":"2024","unstructured":"Das, S., Khan, S.S., Sengupta, D., et al.: LeXNet++: layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images. Neural Comput. Appl. (2024). https:\/\/doi.org\/10.1007\/s00521-024-10441-6","journal-title":"Neural Comput. Appl."},{"issue":"5","key":"11_CR6","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1093\/annonc\/mdz056","volume":"30","author":"B Escudier","year":"2019","unstructured":"Escudier, B., Porta, C., Schmidinger, M., et al.: Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 30(5), 706\u2013720 (2019)","journal-title":"Ann. Oncol."},{"key":"11_CR7","unstructured":"Fuchs, M., Gonzalez, C., Mukhopadhyay, A.: Practical uncertainty quantification for brain tumor segmentation. In: Medical Imaging with Deep Learning (MIDL) (2021)"},{"key":"11_CR8","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML, pp. 1050\u20131059 (2016)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al.: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"11_CR12","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems (NeurIPS), vol. 20 (2017)"},{"key":"11_CR13","unstructured":"Kohl, S., Romera-Paredes, B., Meyer, C., et al.: A probabilistic U-Net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 31 (2018)"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"e081554","DOI":"10.1136\/bmj-2024-081554","volume":"388","author":"K Lekadir","year":"2025","unstructured":"Lekadir, K., Frangi, A.F., Porras, A.R., Glocker, B., Cintas, C., Langlotz, C.P., et al.: FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025)","journal-title":"BMJ"},{"issue":"11","key":"11_CR15","first-page":"3090","volume":"41","author":"R Mehta","year":"2022","unstructured":"Mehta, R., Paunovic, V., Arbel, T.: Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference. IEEE Trans. Med. Imaging 41(11), 3090\u20133102 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR16","unstructured":"Mehta, R.: Integrating Bayesian deep learning uncertainties in medical image analysis. Ph.D. thesis, Department of Electrical & Computer Engineering, McGill University (2023)"},{"key":"11_CR17","first-page":"71833","volume":"83","author":"H Meine","year":"2024","unstructured":"Meine, H., Chlebus, G., Ghafoorian, M., Endo, I., Schenk, A.: Comparison of U-net based convolutional neural networks for liver segmentation in CT. J. Intell. Fuzzy Syst. 83, 71833\u201371862 (2024)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"10","key":"11_CR18","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR19","unstructured":"Monteiro, M., Allken, V., Wang, B., Jacob, M.W.: Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 12756\u201312767 (2020)"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"101557","DOI":"10.1016\/j.media.2019.101557","volume":"59","author":"T Nair","year":"2020","unstructured":"Nair, T., Chen, L., Yang, C., Precup, D.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59, 101557 (2020)","journal-title":"Med. Image Anal."},{"key":"11_CR21","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., et al.: Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018). https:\/\/arxiv.org\/abs\/1804.03999"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Rastegarpoor, M., Cool, D.W., Fenster, A.: Segmentation of kidney ablation zone using deep learning in CT images. In: Proceedings of SPIE, vol. 13406, San Diego, USA (2025)","DOI":"10.1117\/12.3039617"},{"key":"11_CR23","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 \u2014 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"},{"issue":"2","key":"11_CR24","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359 (2019)","journal-title":"Int. J. Comput. Vis."},{"key":"11_CR25","doi-asserted-by":"publisher","unstructured":"Salahuddin, Z., Zhang, T., Wang, Y., Salama, M.S.: Leveraging uncertainty estimation for segmentation of kidney, kidney tumor and kidney cysts. In: International Challenge on Kidney and Kidney Tumor Segmentation, pp. 40\u201346. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-54806-2_6","DOI":"10.1007\/978-3-031-54806-2_6"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Dalca, A., Baumgartner, C.F.: Uncertainty for safe utilization of machine learning in medical imaging. In: MICCAI UNSURE Workshop (2022)","DOI":"10.1007\/978-3-031-16749-2"}],"container-title":["Lecture Notes in Computer Science","Empowering Medical Image Computing and Research Through Early-Career Expertise"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-24182-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:22:51Z","timestamp":1783722171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-24182-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032241818","9783032241825"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-24182-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"4 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EMERGE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Empowering Medical Image Computing and Research through Early-Career Expertise","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","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":"emerge2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccaimsb.github.io\/emerge\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}