{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:28:30Z","timestamp":1757622510334,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031963179"},{"type":"electronic","value":"9783031963186"}],"license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"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\/978-3-031-96318-6_5","type":"book-chapter","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T05:23:24Z","timestamp":1754889804000},"page":"46-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Fetal Biometry Assessment with\u00a0Deep Ensembles Using Sparse-Sampling of\u00a02D Intrapartum Ultrasound Images"],"prefix":"10.1007","author":[{"given":"Jayroop","family":"Ramesh","sequence":"first","affiliation":[]},{"given":"Valentin","family":"Bacher","sequence":"additional","affiliation":[]},{"given":"Mark C.","family":"Eid","sequence":"additional","affiliation":[]},{"given":"Hoda","family":"Kalabizadeh","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Rupprecht","sequence":"additional","affiliation":[]},{"given":"Ana I. L.","family":"Namburete","sequence":"additional","affiliation":[]},{"given":"Pak-Hei","family":"Yeung","sequence":"additional","affiliation":[]},{"given":"Madeleine K.","family":"Wyburd","sequence":"additional","affiliation":[]},{"given":"Nicola K.","family":"Dinsdale","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"issue":"1","key":"5_CR1","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1002\/uog.19072","volume":"52","author":"T Ghi","year":"2018","unstructured":"Ghi, T., et al.: ISUOG practice guidelines: intrapartum ultrasound. Ultrasound Obstet. Gynecol. 52(1), 128\u2013139 (2018)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"940150","DOI":"10.3389\/fphys.2022.940150","volume":"13","author":"J Bai","year":"2022","unstructured":"Bai, J., et al.: A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network. Front. Physiol. 13, 940150 (2022)","journal-title":"Front. Physiol."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"123096","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."},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015). https:\/\/arxiv.org\/abs\/1409.1556","key":"5_CR4"},{"unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). https:\/\/arxiv.org\/abs\/1512.03385","key":"5_CR5"},{"unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks (2017). https:\/\/arxiv.org\/abs\/1605.07146","key":"5_CR6"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van\u00a0der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2018). https:\/\/arxiv.org\/abs\/1608.06993","key":"5_CR7","DOI":"10.1109\/CVPR.2017.243"},{"unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks (2020). https:\/\/arxiv.org\/abs\/1905.11946","key":"5_CR8"},{"unstructured":"Tan, M., Le, Q.V.: EfficientNetV2: smaller models and faster training (2021). https:\/\/arxiv.org\/abs\/2104.00298","key":"5_CR9"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s (2022). https:\/\/arxiv.org\/abs\/2201.03545","key":"5_CR10","DOI":"10.1109\/CVPR52688.2022.01167"},{"doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","key":"5_CR11","DOI":"10.1109\/CVPR.2015.7298965"},{"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","key":"5_CR12","DOI":"10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","key":"5_CR13","DOI":"10.1109\/TMI.2019.2959609"},{"doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp. 801\u2013818 (2018)","key":"5_CR14","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","volume":"8","author":"T Fan","year":"2020","unstructured":"Fan, T., Wang, G., Li, Y., Wang, H.: Ma-net: A multi-scale attention network for liver and tumor segmentation. IEEE Access 8, 179656\u2013179665 (2020)","journal-title":"IEEE Access"},{"unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Advances in Neural Information Processing Systems 30 (2017)","key":"5_CR16"},{"key":"5_CR17","doi-asserted-by":"publisher","first-page":"109131","DOI":"10.1016\/j.patcog.2022.109131","volume":"135","author":"X Yu","year":"2023","unstructured":"Yu, X., Wang, J., Zhao, Y., Gao, Y.: Mix-ViT: mixing attentive vision transformer for ultra-fine-grained visual categorization. Pattern Recogn. 135, 109131 (2023)","journal-title":"Pattern Recogn."},{"key":"5_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1007\/978-3-030-20893-6_41","volume-title":"Computer Vision \u2013 ACCV 2018","author":"M Dawson","year":"2019","unstructured":"Dawson, M., Zisserman, A., Nell\u00e5ker, C.: From same photo: cheating on visual kinship challenges. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 654\u2013668. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_41"},{"doi-asserted-by":"publisher","unstructured":"Wightman, R.: PyTorch image models. https:\/\/github.com\/rwightman\/pytorch-image-models (2019). https:\/\/doi.org\/10.5281\/zenodo.4414861","key":"5_CR19","DOI":"10.5281\/zenodo.4414861"},{"unstructured":"Iakubovskii, P.: Segmentation models PyTorch. https:\/\/github.com\/qubvel\/segmentation_models.pytorch (2019)","key":"5_CR20"},{"unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems 30 (2017)","key":"5_CR21"},{"unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems 30 (2017)","key":"5_CR22"},{"unstructured":"Fort, S., Hu, H., Lakshminarayanan, B.: Deep ensembles: a loss landscape perspective. arXiv preprint: arXiv:1912.02757 (2019)","key":"5_CR23"},{"doi-asserted-by":"crossref","unstructured":"Valdenegro-Toro, M., Mori, D.S.: A deeper look into aleatoric and epistemic uncertainty disentanglement. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1508\u20131516. IEEE (2022)","key":"5_CR24","DOI":"10.1109\/CVPRW56347.2022.00157"}],"container-title":["Lecture Notes in Computer Science","Intrapartum Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96318-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T20:12:48Z","timestamp":1757362368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96318-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,12]]},"ISBN":["9783031963179","9783031963186"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96318-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,12]]},"assertion":[{"value":"12 August 2025","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":"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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iugc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/codalab.lisn.upsaclay.fr\/competitions\/18413","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}