{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:45:19Z","timestamp":1767311119697,"version":"3.48.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032116154","type":"print"},{"value":"9783032116161","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\/978-3-032-11616-1_10","type":"book-chapter","created":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:42:21Z","timestamp":1767310941000},"page":"105-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heatmap Regression for Automated Angle of Progression Measurement: The Baseline Method for the IUGC2025"],"prefix":"10.1007","author":[{"given":"Yitong","family":"Tang","sequence":"first","affiliation":[]},{"given":"Zihao","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yaosheng","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jieyun","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Shun","family":"Long","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Isaac","family":"Khobo","sequence":"additional","affiliation":[]},{"given":"Shun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zimo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"1","key":"10_CR1","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1093\/emph\/eoy006","volume":"2018","author":"KR Rosenberg","year":"2018","unstructured":"Rosenberg, K.R., Trevathan, W.R.: Evolutionary perspectives on cesarean section. Evol. Med. Public Health 2018(1), 67\u201381 (2018)","journal-title":"Evol. Med. Public Health"},{"issue":"01","key":"10_CR2","doi-asserted-by":"publisher","first-page":"07","DOI":"10.1055\/s-0031-1285829","volume":"29","author":"KD Gregory","year":"2012","unstructured":"Gregory, K.D., Jackson, S., Korst, L., Fridman, M.: Cesarean versus vaginal delivery: whose risks? whose benefits? Am. J. Perinatol. 29(01), 07\u201318 (2012)","journal-title":"Am. J. Perinatol."},{"issue":"10155","key":"10_CR3","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1016\/S0140-6736(18)31930-5","volume":"392","author":"J Sandall","year":"2018","unstructured":"Sandall, J., Tribe, R.M., Avery, L., Mola, G., Visser, G.H., Homer, C.S., Gibbons, D., Kelly, N.M., Kennedy, H.P., Kidanto, H., et al.: Short-term and long-term effects of caesarean section on the health of women and children. The Lancet 392(10155), 1349\u20131357 (2018)","journal-title":"The Lancet"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Seval, M.M., Yuce, T., Kalafat, E., Duman, B., Aker, S., Kumbasar, H., Koc, A.: Comparison of effects of digital vaginal examination with transperineal ultrasound during labor on pain and anxiety levels: a randomized controlled trial (2016)","DOI":"10.1002\/uog.15994"},{"issue":"4","key":"10_CR5","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1002\/uog.12422","volume":"41","author":"B Tutschek","year":"2013","unstructured":"Tutschek, B., Torkildsen, E., Eggeb\u00f8, T.: Comparison between ultrasound parameters and clinical examination to assess fetal head station in labor. Ultrasound Obstet. Gynecol. 41(4), 425\u2013429 (2013)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Tutschek, B., Braun, T., Chantraine, F., Henrich, W.: A study of progress of labour using intrapartum translabial ultrasound, assessing head station, direction, and angle of descent. BJOG Int. J. Obstet. Gynaecol. 118(1), 62\u201369 (2011)","DOI":"10.1111\/j.1471-0528.2010.02775.x"},{"issue":"4","key":"10_CR7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1159\/000457124","volume":"42","author":"F Bellussi","year":"2017","unstructured":"Bellussi, F., Ghi, T., Youssef, A., Cataneo, I., Salsi, G., Simonazzi, G., Pilu, G.: Intrapartum ultrasound to differentiate flexion and deflexion in occipitoposterior rotation. Fetal Diagn. Ther. 42(4), 249\u2013256 (2017)","journal-title":"Fetal Diagn. Ther."},{"issue":"5","key":"10_CR8","doi-asserted-by":"publisher","first-page":"520","DOI":"10.3109\/14767058.2013.825598","volume":"27","author":"A Malvasi","year":"2014","unstructured":"Malvasi, A., Tinelli, A., Barbera, A., Eggeb\u00f8, T., Mynbaev, O., Bochicchio, M., Pacella, E., Di Renzo, G.: Occiput posterior position diagnosis: vaginal examination or intrapartum sonography? a clinical review. J. Matern. Fetal Neonatal Med. 27(5), 520\u2013526 (2014)","journal-title":"J. Matern. Fetal Neonatal Med."},{"issue":"4","key":"10_CR9","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1159\/000448947","volume":"41","author":"A Youssef","year":"2017","unstructured":"Youssef, A., Salsi, G., Montaguti, E., Bellussi, F., Pacella, G., Azzarone, C., Farina, A., Rizzo, N., Pilu, G.: Automated measurement of the angle of progression in labor: a feasibility and reliability study. Fetal Diagn. Ther. 41(4), 293\u2013299 (2017)","journal-title":"Fetal Diagn. Ther."},{"issue":"1","key":"10_CR10","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1002\/uog.22159","volume":"58","author":"A Youssef","year":"2021","unstructured":"Youssef, A., Brunelli, E., Azzarone, C., Di Donna, G., Casadio, P., Pilu, G.: Fetal head progression and regression on maternal pushing at term and labor outcome. Ultrasound Obstet. Gynecol. 58(1), 105\u2013110 (2021)","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1002\/uog.19072","volume":"52","author":"T Ghi","year":"2018","unstructured":"Ghi, T., Eggeb\u00f8, T., Lees, C., Kalache, K., Rozenberg, P., Youssef, A., Salomon, L., Tutschek, B.: Isuog practice guidelines: intrapartum ultrasound. Ultrasound Obstet. Gynecol. 52(1), 128\u2013139 (2018)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2022.107904","volume":"41","author":"Y Lu","year":"2022","unstructured":"Lu, Y., Zhou, M., Zhi, D., Zhou, M., Jiang, X., Qiu, R., Ou, Z., Wang, H., Qiu, D., Zhong, M., et al.: The JNU-IFM dataset for segmenting pubic symphysis-fetal head. Data Brief 41, 107904 (2022)","journal-title":"Data Brief"},{"issue":"4","key":"10_CR13","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TMI.2013.2276943","volume":"33","author":"S Rueda","year":"2013","unstructured":"Rueda, S., Fathima, S., Knight, C.L., Yaqub, M., Papageorghiou, A.T., Rahmatullah, B., Foi, A., Maggioni, M., Pepe, A., Tohka, J., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33(4), 797\u2013813 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Dietz, H.P.: Ultrasound imaging of the pelvic floor part i: two-dimensional aspects. Ultrasound Obstet. Gynecol. 23(1), 80\u201392 (2004)","DOI":"10.1002\/uog.939"},{"key":"10_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123096","volume":"245","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Ou, Z., Lu, Y., Bai, J.: Direction-guided and multi-scale feature screening for fetal head-pubic symphysis segmentation and angle of progression calculation. Expert Syst. Appl. 245, 123096 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"10_CR16","doi-asserted-by":"publisher","first-page":"4648","DOI":"10.1109\/JBHI.2024.3399762","volume":"28","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Lu, Y., Long, S., Campello, V.M., Bai, J., Lekadir, K.: Fetal head and pubic symphysis segmentation in intrapartum ultrasound image using a dual-path boundary-guided residual network. IEEE J. Biomed. Health Inform. 28(8), 4648\u20134659 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10_CR17","first-page":"406","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020","author":"M Zhou","year":"2020","unstructured":"Zhou, M., Yuan, C., Chen, Z., Wang, C., Lu, Y.: Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020, pp. 406\u2013414. Springer International Publishing, Cham (2020)"},{"issue":"1","key":"10_CR18","first-page":"5192338","volume":"2022","author":"Y Lu","year":"2022","unstructured":"Lu, Y., Zhi, D., Zhou, M., Lai, F., Chen, G., Ou, Z., Zeng, R., Long, S., Qiu, R., Zhou, M., Jiang, X., Wang, H., Bai, J.: Multitask deep neural network for the fully automatic measurement of the angle of progression. Comput. Math. Methods Med. 2022(1), 5192338 (2022)","journal-title":"Comput. Math. Methods Med."},{"key":"10_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125699","volume":"263","author":"Z Zhou","year":"2025","unstructured":"Zhou, Z., Lu, Y., Bai, J., Campello, V.M., Feng, F., Lekadir, K.: Segment anything model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis. Expert Syst. Appl. 263, 125699 (2025)","journal-title":"Expert Syst. Appl."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Thaler, F., Payer, C., Urschler, M., Stern, D.: Modeling annotation uncertainty with gaussian heatmaps in landmark localization. arXiv preprint arXiv:2109.09533 (2021)","DOI":"10.59275\/j.melba.2021-77a7"},{"issue":"3","key":"10_CR21","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1109\/TSMC.1986.4308985","volume":"16","author":"H Marmolin","year":"1986","unstructured":"Marmolin, H.: Subjective MSE measures. IEEE Trans. Syst. Man Cybern. 16(3), 486\u2013489 (1986)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., Zhang, Y., Gu, S.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)","journal-title":"Med. Image Anal."},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Proceedings, part III, vol. 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Intrapartum Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11616-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:42:22Z","timestamp":1767310942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11616-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032116154","9783032116161"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11616-1_10","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IUGC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Intrapartum Ultrasound Grand Challenge","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 (Democratic People's 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":"iugc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/zenodo.org\/records\/15081529","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}