Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.
Authors
Bai J,Zhou Z,Tang Y,Gan J,Liang Z,Fan J,Mcguire LB,Clarke JL,Cai W,Spurway J,Tan Y,Wang S,Shen W,Yu W,Li Y,Zhang P,Jiang W,Li Y,Al Nasi SMAB,Abzhanov A,Saeed N,Yaqub M,Xia Z,Li H,Lan L,Ramesh J,Bacher V,Eid M,Kalabizadeh H,Rupprecht C,Namburete AIL,Yeung PH,Wyburd MK,Dinsdale NK,Serikbey A,Li J,Chen SL,Hu Z,Liu N,Deng Y,Hu W,Tan C,Zhang W,Nhi MT,Koehler G,Stock R,Maier-Hein K,Elbatel M,Li X,Slimani S,Campello VM,Ohene-Botwe B,Khobo I,Huang Y,Han Z,Hou H,Qiu D,Zheng Z,Luo G,Ni D,Lu Y,Lekadir K,Li S
Affiliations (60)
Affiliations (60)
- 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. Electronic address: [email protected].
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University Jinan University, Guangzhou, China.
- School of Computer Science, University of Sydney, Sydney, Australia. Electronic address: [email protected].
- School of Computer Science, University of Sydney, Sydney, Australia. Electronic address: [email protected].
- School of Computer Science, University of Sydney, Sydney, Australia. Electronic address: [email protected].
- Discipline of Obstetrics, Gynaecology and Neonatology, Sydney Medical School Nepean, University of Sydney Nepean Hospital, Penrith, New South Wales, Australia. Electronic address: [email protected].
- Discipline of Medical Imaging, Faculty of Medicine and Health, Susan Wakil Health Building, University of Sydney, Camperdown, New South Wales, Australia. Electronic address: [email protected].
- School of Computer Science, University of Sydney, Sydney, Australia. Electronic address: [email protected].
- Medical Imaging, Orange Health Service, Orange, New South Wales, Australia. Electronic address: [email protected].
- University of Electronic Science and Technology of China, Chengdu, China. Electronic address: [email protected].
- Henan Kaifeng College of Science Technology and Communication, Kaifeng, China.
- Changchun University of Science and Technology, Changchun, China.
- University of Electronic Science and Technology of China, Chengdu, China.
- United Imaging Healthcare, Shanghai, China.
- University of Western Brittany, Brest, France.
- Sichuan University, Chengdu, China.
- University of Electronic Science and Technology of China, Chengdu, China. Electronic address: [email protected].
- Department of Machine Learning, Mohamed bin Zayed, University of Artificial Intelligence, Masdar, Abu Dhabi, UAE. Electronic address: [email protected].
- Department of Machine Learning, Mohamed bin Zayed, University of Artificial Intelligence, Masdar, Abu Dhabi, UAE. Electronic address: [email protected].
- Department of Machine Learning, Mohamed bin Zayed, University of Artificial Intelligence, Masdar, Abu Dhabi, UAE. Electronic address: [email protected].
- Department of Machine Learning, Mohamed bin Zayed, University of Artificial Intelligence, Masdar, Abu Dhabi, UAE. Electronic address: [email protected].
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China. Electronic address: [email protected].
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China. Electronic address: [email protected].
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Visual Geometry Group, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Visual Geometry Group, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- School of Computer Science and Engineering, Nanyang Technological University, Singapore. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- Department of Computer Science, Oxford Machine Learning in NeuroImaging Lab, University of Oxford, Oxford, United Kingdom. Electronic address: [email protected].
- The University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].
- The University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].
- The University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China. Electronic address: [email protected].
- College of Computer and Information Science, Chongqing Normal University, Chongqing, China.
- The University of Manchester, Manchester, United Kingdom. Electronic address: [email protected].
- College of Computer and Information Science, Chongqing Normal University, Chongqing, China. Electronic address: [email protected].
- College of Computer and Information Science, Chongqing Normal University, Chongqing, China. Electronic address: [email protected].
- Southwest University, Chongqing, China.
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: [email protected].
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: [email protected].
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: [email protected].
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hongkong, China. Electronic address: [email protected].
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hongkong, China. Electronic address: [email protected].
- Chief Medical Officer Deepecho Ibn Rochd CHU, Hassan II University, Casablanca, Morocco. Electronic address: [email protected].
- Universitat de Barcelona, Barcelona, Spain. Electronic address: [email protected].
- Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana. Electronic address: [email protected].
- Department of Human Biology, Biomedical Engineering Research Center, University of Cape Town, Cape Town, South Africa. Electronic address: [email protected].
- Obstetrics and Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Department of Obstetrics and Gynecology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Electronic address: [email protected].
- Department of Obstetrics and Gynecology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
- Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center, for Child Health, Guangzhou, China. Electronic address: [email protected].
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. Electronic address: [email protected].
- Shenzhen University, Shenzhen, China. Electronic address: [email protected].
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University Jinan University, Guangzhou, China. Electronic address: [email protected].
- Institució Catalana de Recerca i Estudis Avançats (ICREA) and Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain. Electronic address: [email protected].
- School of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. Electronic address: [email protected].
Abstract
A significant proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, particularly prevalent in low- and middle-income countries. Intrapartum biometry plays a crucial role in monitoring labor progress. However, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To tackle this issue, the Intrapartum Ultrasound Grand Challenge (IUGC), co-hosted with MICCAI 2024, was launched. The IUGC designed a multi-task automatic measurement framework oriented towards clinical applications. This framework integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry, enabling algorithms to leverage complementary information for more accurate estimations. Moreover, the challenge introduced the largest multi-center intrapartum ultrasound video dataset, consisting of 774 videos (68,106 images) collected from three hospitals. This rich dataset provides a solid foundation for algorithm training and evaluation. In this study, we elaborate on the details of the challenge, review the works submitted by eight teams, and interpret their methods from five aspects: preprocessing, data augmentation, learning strategy, model architecture, and post-processing. Additionally, we analyze the results considering various factors to identify key obstacles, explore potential solutions, and highlight ongoing challenges for future research. We conclude that although promising results have been achieved, the research remains in its early stages, and further in-depth exploration is required before clinical implementation. The solutions and the complete dataset are publicly accessible, aiming to drive continuous advancements in automatic biometry for intrapartum ultrasound imaging.
Topics
Deep LearningUltrasonography, PrenatalBiometryImage Interpretation, Computer-AssistedJournal Article