{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:22:06Z","timestamp":1768994526218,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556335","type":"print"},{"value":"9789819556342","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\/978-981-95-5634-2_17","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:31Z","timestamp":1768944211000},"page":"238-253","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coherence-Based Segmentation Quality Evaluator Trained on\u00a0a\u00a0Large Collection of\u00a0Annotated Medical Images"],"prefix":"10.1007","author":[{"given":"Ahjol","family":"Senbi","sequence":"first","affiliation":[]},{"given":"Tianyu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuhui","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chengyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yizhe","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"17_CR1","unstructured":"Alfasly, S., et\u00a0al.: When is a foundation model a foundation model. arXiv preprint arXiv:2309.11510 (2023)"},{"key":"17_CR2","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"17_CR3","unstructured":"Chen, Y., Zhou, Z., Yuille, A.: Quality sentinel: estimating label quality and errors in medical segmentation datasets. arXiv preprint arXiv:2406.00327 (2024)"},{"key":"17_CR4","unstructured":"Cheng, J., et\u00a0al.: SAM-Med2D. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"17_CR5","unstructured":"DeVries, T., Taylor, G.W.: Leveraging uncertainty estimates for predicting segmentation quality. arXiv preprint arXiv:1807.00502 (2018)"},{"key":"17_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106389","volume":"155","author":"H Gong","year":"2023","unstructured":"Gong, H., Chen, J., Chen, G., Li, H., Li, G., Chen, F.: Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput. Biol. Med. 155, 106389 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"17_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.2478\/v10117-011-0021-1","volume":"30","author":"J Hauke","year":"2011","unstructured":"Hauke, J., Kossowski, T.: Comparison of values of pearson\u2019s and spearman\u2019s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2), 87\u201393 (2011)","journal-title":"Quaestiones Geographicae"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Huang, C., Wu, Q., Meng, F.: QualityNet: Segmentation quality evaluation with deep convolutional networks. In: 2016 Visual Communications and Image Processing (VCIP), pp.\u00a01\u20134. IEEE (2016)","DOI":"10.1109\/VCIP.2016.7805585"},{"key":"17_CR9","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"17_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102426","volume":"78","author":"K Li","year":"2022","unstructured":"Li, K., Yu, L., Heng, P.A.: Towards reliable cardiac image segmentation: assessing image-level and pixel-level segmentation quality via self-reflective references. Med. Image Anal. 78, 102426 (2022)","journal-title":"Med. Image Anal."},{"key":"17_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Fast and low-gpu-memory abdomen ct organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)","journal-title":"Med. Image Anal."},{"issue":"1","key":"17_CR12","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Pedraza, L., Vargas, C., Narv\u00e1ez, F., Dur\u00e1n, O., Mu\u00f1oz, E., Romero, E.: An open access thyroid ultrasound image database. In: 10th International Symposium on Medical Information Processing and Analysis. vol.\u00a09287, pp. 188\u2013193. SPIE (2015)","DOI":"10.1117\/12.2073532"},{"issue":"2","key":"17_CR14","doi-asserted-by":"publisher","first-page":"3030","DOI":"10.1109\/LRA.2022.3143219","volume":"7","author":"QM Rahman","year":"2022","unstructured":"Rahman, Q.M., S\u00fcnderhauf, N., Corke, P., Dayoub, F.: FSNet: a failure detection framework for semantic segmentation. IEEE Robot. Automation Lett. 7(2), 3030\u20133037 (2022)","journal-title":"IEEE Robot. Automation Lett."},{"issue":"2","key":"17_CR15","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1038\/s41592-023-02150-0","volume":"21","author":"A Reinke","year":"2024","unstructured":"Reinke, A., et al.: Understanding metric-related pitfalls in image analysis validation. Nat. Methods 21(2), 182\u2013194 (2024)","journal-title":"Nat. Methods"},{"key":"17_CR16","doi-asserted-by":"publisher","unstructured":"Robinson, R., et al.: Real-time prediction of segmentation quality. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 578\u2013585. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_66","DOI":"10.1007\/978-3-030-00937-3_66"},{"key":"17_CR17","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Rottmann, M., et al.: Prediction error meta classification in semantic segmentation: detection via aggregated dispersion measures of softmax probabilities. In: IJCNN, pp.\u00a01\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206659"},{"issue":"8","key":"17_CR19","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":"17_CR20","doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: Deep generative model-based quality control for cardiac MRI segmentation. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 88\u201397. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_9","DOI":"10.1007\/978-3-030-59719-1_9"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Wunderling, T., Golla, B., Poudel, P., Arens, C., Friebe, M., Hansen, C.: Comparison of thyroid segmentation techniques for 3d ultrasound. In: Medical Imaging 2017: Image Processing. vol. 10133, pp. 346\u2013352. SPIE (2017)","DOI":"10.1117\/12.2254234"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Wundram, A.M., Fischer, P., Muehlebach, M., Koch, L.M., Baumgartner, C.F.: Conformal performance range prediction for segmentation output quality control. arXiv preprint arXiv:2407.13307 (2024)","DOI":"10.1007\/978-3-031-73158-7_8"},{"key":"17_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104791","volume":"84","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X., Zhang, Q.: Transformers in medical image segmentation: a review. Biomed. Sign. Process. Control 84, 104791 (2023)","journal-title":"Biomed. Sign. Process. Control"},{"key":"17_CR24","unstructured":"Ye, J., et\u00a0al.: Sa-med2d-20m dataset: segment anything in 2d medical imaging with 20 million masks. arXiv preprint arXiv:2311.11969 (2023)"},{"key":"17_CR25","unstructured":"Zhao, Z., et al.: One model to rule them all: towards universal segmentation for medical images with text prompts. arXiv preprint arXiv:2312.17183 (2023)"},{"key":"17_CR26","unstructured":"Zhou, L., Deng, W., Wu, X.: Robust image segmentation quality assessment. Med. Imaging Deep Learn. (2020)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5634-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:32Z","timestamp":1768944212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5634-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556335","9789819556342"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5634-2_17","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":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}