{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:40:26Z","timestamp":1727912426139},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031731570","type":"print"},{"value":"9783031731587","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73158-7_8","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:02:04Z","timestamp":1727910124000},"page":"81-91","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conformal Performance Range Prediction for\u00a0Segmentation Output Quality Control"],"prefix":"10.1007","author":[{"given":"Anna M.","family":"Wundram","sequence":"first","affiliation":[]},{"given":"Paul","family":"Fischer","sequence":"additional","affiliation":[]},{"given":"Michael","family":"M\u00fchlebach","sequence":"additional","affiliation":[]},{"given":"Lisa M.","family":"Koch","sequence":"additional","affiliation":[]},{"given":"Christian F.","family":"Baumgartner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"8_CR1","unstructured":"Angelopoulos, A.N., Bates, S.: A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511 (2021)"},{"key":"8_CR2","unstructured":"Angelopoulos, A.N., et al.: Image-to-image regression with distribution-free uncertainty quantification and applications in imaging. In: International Conference on Machine Learning, pp. 717\u2013730. PMLR (2022)"},{"key":"8_CR3","unstructured":"Ayhan, M.S., Berens, P.: Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In: Medical Imaging with Deep Learning (2018)"},{"key":"8_CR4","unstructured":"Azad, R., et al.: Medical image segmentation review: the success of u-net. arXiv preprint arXiv:2211.14830 (2022)"},{"issue":"2","key":"8_CR5","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1214\/23-AOS2276","volume":"51","author":"RF Barber","year":"2023","unstructured":"Barber, R.F., Candes, E.J., Ramdas, A., Tibshirani, R.J.: Conformal prediction beyond exchangeability. Ann. Stat. 51(2), 816\u2013845 (2023)","journal-title":"Ann. Stat."},{"key":"8_CR6","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: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II","author":"CF Baumgartner","year":"2019","unstructured":"Baumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II, pp. 119\u2013127. Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_14"},{"issue":"11","key":"8_CR7","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Cho, W., Park, J., Choo, J.: Training auxiliary prototypical classifiers for explainable anomaly detection in medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2624\u20132633 (2023)","DOI":"10.1109\/WACV56688.2023.00265"},{"key":"8_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102213","volume":"74","author":"J Fournel","year":"2021","unstructured":"Fournel, J., et al.: Medical image segmentation automatic quality control: a multi-dimensional approach. Med. Image Anal. 74, 102213 (2021)","journal-title":"Med. Image Anal."},{"key":"8_CR10","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"8_CR11","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-030-80432-9_22","volume-title":"Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12\u201314, 2021, Proceedings","author":"E Hann","year":"2021","unstructured":"Hann, E., Gonzales, R.A., Popescu, I.A., Zhang, Q., Ferreira, V.M., Piechnik, S.K.: Ensemble of deep convolutional neural networks with monte Carlo dropout sampling for automated image segmentation quality control and robust deep learning using small datasets. In: Papie\u017c, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12\u201314, 2021, Proceedings, pp. 280\u2013293. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80432-9_22"},{"key":"8_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108593","volume":"334","author":"WG Herrera","year":"2020","unstructured":"Herrera, W.G., Pereira, M., Bento, M., Lapa, A.T., Appenzeller, S., Rittner, L.: A framework for quality control of corpus callosum segmentation in large-scale studies. J. Neurosci. Methods 334, 108593 (2020)","journal-title":"J. Neurosci. Methods"},{"issue":"2","key":"8_CR14","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., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"1","key":"8_CR15","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41597-022-01564-3","volume":"9","author":"K Jin","year":"2022","unstructured":"Jin, K., et al.: Fives: a fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data 9(1), 475 (2022)","journal-title":"Sci. Data"},{"key":"8_CR16","unstructured":"Kahl, K.C., L\u00fcth, C.T., Zenk, M., Maier-Hein, K., Jaeger, P.F.: Values: a framework for systematic validation of uncertainty estimation in semantic segmentation. arXiv preprint arXiv:2401.08501 (2024)"},{"key":"8_CR17","unstructured":"Kohl, S., et al.: A probabilistic U-Net for segmentation of ambiguous images. In: International Conference on Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018)"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-642-33415-3_65","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012","author":"T Kohlberger","year":"2012","unstructured":"Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012, pp. 528\u2013536. Springer, Berlin, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33415-3_65"},{"key":"8_CR19","unstructured":"K\u00f6hler, P., Fadugba, J., Berens, P., Koch, L.M.: Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images. In: Medical Imaging with Deep Learning \u2013 MIDL (2024)"},{"key":"8_CR20","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles (2017)"},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/978-3-031-16449-1_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part VII","author":"Z Li","year":"2022","unstructured":"Li, Z., Kamnitsas, K., Islam, M., Chen, C., Glocker, B.: Estimating model performance under domain shifts with\u00a0class-specific confidence scores. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part VII, pp. 693\u2013703. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_66"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Lin, Q., Chen, X., Chen, C., Garibaldi, J.M.: A novel quality control algorithm for medical image segmentation based on fuzzy uncertainty. IEEE Trans. Fuzzy Syst., 2532\u20132544 (2022)","DOI":"10.1109\/TFUZZ.2022.3228332"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1151\u20131161 (2023)","DOI":"10.1109\/ICCV51070.2023.00112"},{"key":"8_CR24","unstructured":"Ng, M., Guo, F., Biswas, L., Wright, G.A.: Estimating uncertainty in neural networks for segmentation quality control. In: 32nd International Conference on Neural Information Processing Systems (NIPS 2018), Montr\u00e9al, Canada, no. NIPS, pp.\u00a03\u20136 (2018)"},{"issue":"1","key":"8_CR25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s12968-020-00650-y","volume":"22","author":"E Puyol-Ant\u00f3n","year":"2020","unstructured":"Puyol-Ant\u00f3n, E., et al.: Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control. J. Cardiovasc. Magn. Reson. 22(1), 60 (2020)","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Robinson, R., et al.: Real-time prediction of segmentation quality. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018, pp. 578\u2013585. Springer International Publishing, Cham (2018)","DOI":"10.1007\/978-3-030-00937-3_66"},{"key":"8_CR27","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: International Conference on Neural Information Processing Systems, vol. 28 (2015)"},{"issue":"9","key":"8_CR28","doi-asserted-by":"publisher","first-page":"714","DOI":"10.3390\/diagnostics10090714","volume":"10","author":"MR Sunoqrot","year":"2020","unstructured":"Sunoqrot, M.R., et al.: A quality control system for automated prostate segmentation on T2-weighted MRI. Diagnostics 10(9), 714 (2020)","journal-title":"Diagnostics"},{"issue":"8","key":"8_CR29","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2017.2665165","volume":"36","author":"VV Valindria","year":"2017","unstructured":"Valindria, V.V., et al.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imaging 36(8), 1597\u20131606 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR30","doi-asserted-by":"publisher","unstructured":"Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world, vol.\u00a029. Springer (2005). https:\/\/doi.org\/10.1007\/b106715","DOI":"10.1007\/b106715"},{"key":"8_CR31","unstructured":"Williams, E., et\u00a0al.: Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows. arXiv preprint arXiv:2112.03277 (2021)"},{"key":"8_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-319-46630-9_14","volume-title":"Simulation and Synthesis in Medical Imaging","author":"L Zhang","year":"2016","unstructured":"Zhang, L., et al.: Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 138\u2013145. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46630-9_14"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73158-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:07:10Z","timestamp":1727910430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73158-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"ISBN":["9783031731570","9783031731587"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73158-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors\u00a0have 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":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","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":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}
