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arXiv 2602.12922cs.CV
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超越IUGC基准：重新思考深度学习方法在胎儿超声视频中的产程超声生物测量需求

Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos

Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China；Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand；School of Computer Science, University of Sydney, Sydney, Australia；Neonatology, Sydney Medical School Nepean, University of Sydney Nepean Hospital, Penrith, New South Wales, Australia；Discipline of Medical Imaging, Faculty of Medicine；Health, Susan Wakil Health Building, University of Sydney, Camperdown, New South Wales, Australia；Medical Imaging, Orange Health Service, Orange, New South Wales, Australia；University of Electronic Science；Henan Kaifeng College of Science Technology；Changchun University of Science；University of Western Brittany, Brest, France；Sichuan University, Chengdu, China；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Masdar, Abu Dhabi；College of Computer Science；Engineering, Chongqing University of Technology, Chongqing, China；Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford, Oxford, United Kingdom；Visual Geometry Group, University of Oxford, Oxford, United Kingdom；School of Computer Science；Engineering, Nanyang Technological University, Singapore；The University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China；College of Computer；Information Science, Chongqing Normal University, Chongqing, China；The University of Manchester, Manchester, United Kingdom；Southwest University, Chongqing, China；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany；Department of Electronic；Computer Engineering, The Hong Kong University of Science；Chief Medical Officer Deepecho Ibn Rochd CHU, Hassan II University, Casablanca, Morocco；Department of Radiography, School of Biomedical；Allied Health Sciences, College of Health Sciences, University of Ghana, Accra；Department of Human Biology, Biomedical Engineering Research Center, University of Cape Town, Cape Town, South Africa；Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China；Department of Obstetrics；Gynecology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China；Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, China；Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China；Engineering Division, King Abdullah University of Science；Shenzhen University, Shenzhen, China；Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain；School of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
Jieyun Bai, Zihao Zhou, Yitong Tang, Jie Gan, Zhuonan Liang, Jianan Fan, Lisa B. Mcguire, Jillian L. Clarke, Weidong Cai, Jacaueline Spurway, Yubo Tang, Shiye W… 展开作者

Jieyun Bai, Zihao Zhou, Yitong Tang, Jie Gan, Zhuonan Liang, Jianan Fan, Lisa B. Mcguire, Jillian L. Clarke, Weidong Cai, Jacaueline Spurway, Yubo Tang, Shiye Wang, Wenda Shen, Wangwang Yu, Yihao Li, Philippe Zhang, Weili Jiang, Yongjie Li, Salem Muhsin Ali Binqahal Al Nasim, Arsen Abzhanov, Numan Saeed, Mohammad Yaqub, Zunhui Xian, Hongxing Lin, Libin Lan, Jayroop Ramesh, Valentin Bacher, Mark Eid, Hoda Kalabizadeh, Christian Rupprecht, Ana I. L. Namburete, Pak-Hei Yeung, Madeleine K. Wyburd, Nicola K. Dinsdale, Assanali Serikbey, Jiankai Li, Sung-Liang Chen, Zicheng Hu, Nana Liu, Yian Deng, Wei Hu, Cong Tan, Wenfeng Zhang, Mai Tuyet Nhi, Gregor Koehler, Rapheal Stock, Klaus Maier-Hein, Marawan Elbatel, Xiaomeng Li, Saad Slimani, Victor M. Campello, Benard Ohene-Botwe, Isaac Khobo, Yuxin Huang, Zhenyan Han, Hongying Hou, Di Qiu, Zheng Zheng, Gongning Luo, Dong Ni, Yaosheng Lu, Karim Lekadir, Shuo Li

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AI总结：

本研究提出了一种多任务自动测量框架，用于产程超声生物测量，旨在解决资源有限环境下超声技师短缺的问题，并通过公开数据集和基准结果促进该领域的发展。

摘要：

产妇死亡、新生儿死亡和胎死腹中在产程阶段占较大比例（45%），特别是在低收入和中等收入国家，其负担尤为严重。产程生物测量在监测分娩进展中起着关键作用；然而，在资源有限的环境中，超声的常规使用受到受过训练的超声技师短缺的阻碍。为了解决这一挑战，与MICCAI 2024联合举办的产程超声大挑战（IUGC）被启动。IUGC引入了一个以临床为导向的多任务自动测量框架，整合了标准平面分类、胎儿头-耻骨联合分割和生物测量，使算法能够利用互补的任务信息以更精确地进行估计。此外，该挑战发布了迄今为止最大的多中心产程超声视频数据集，包含774个视频（68,106帧），来自三家医院，为模型训练和评估提供了坚实的基础。在本研究中，我们对挑战的设计进行了全面概述，回顾了八个参与团队的提交内容，并从预处理、数据增强、学习策略、模型架构和后处理五个方面分析了他们的方法。此外，我们还对基准结果进行了系统分析，以识别关键瓶颈，探索潜在解决方案，并突出未来研究中的开放挑战。尽管取得了令人鼓舞的性能，我们的发现表明该领域仍处于早期阶段，在大规模临床应用之前还需要进一步深入研究。所有基准解决方案和完整数据集已公开发布，以促进可重复研究并推动自动产程超声生物测量的持续进步。

英文摘要：

A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income countries. Intrapartum biometry plays a critical role in monitoring labor progression; however, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To address this challenge, the Intrapartum Ultrasound Grand Challenge (IUGC), co-hosted with MICCAI 2024, was launched. The IUGC introduces a clinically oriented multi-task automatic measurement framework that integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry, enabling algorithms to exploit complementary task information for more accurate estimation. Furthermore, the challenge releases the largest multi-center intrapartum ultrasound video dataset to date, comprising 774 videos (68,106 frames) collected from three hospitals, providing a robust foundation for model training and evaluation. In this study, we present a comprehensive overview of the challenge design, review the submissions from eight participating teams, and analyze their methods from five perspectives: preprocessing, data augmentation, learning strategy, model architecture, and post-processing. In addition, we perform a systematic analysis of the benchmark results to identify key bottlenecks, explore potential solutions, and highlight open challenges for future research. Although encouraging performance has been achieved, our findings indicate that the field remains at an early stage, and further in-depth investigation is required before large-scale clinical deployment. All benchmark solutions and the complete dataset have been publicly released to facilitate reproducible research and promote continued advances in automatic intrapartum ultrasound biometry.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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