PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images.

Angle of progress Challenge Deep learning Fetal biometry Image segmentation Intrapartum ultrasound

Journal

Medical image analysis
ISSN: 1361-8423
Titre abrégé: Med Image Anal
Pays: Netherlands
ID NLM: 9713490

Informations de publication

Date de publication:
21 Sep 2024
Historique:
received: 02 05 2024
revised: 13 09 2024
accepted: 16 09 2024
medline: 29 9 2024
pubmed: 29 9 2024
entrez: 28 9 2024
Statut: aheadofprint

Résumé

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

Identifiants

pubmed: 39340971
pii: S1361-8415(24)00278-0
doi: 10.1016/j.media.2024.103353
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103353

Informations de copyright

Copyright © 2024 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jieyun Bai (J)

Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China; Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. Electronic address: jbai996@aucklanduni.ac.nz.

Zihao Zhou (Z)

Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China.

Zhanhong Ou (Z)

Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China.

Gregor Koehler (G)

Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Raphael Stock (R)

Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Klaus Maier-Hein (K)

Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Marawan Elbatel (M)

Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hongkong, China.

Robert Martí (R)

Computer Vision and Robotics Group, University of Girona, Girona, Spain.

Xiaomeng Li (X)

Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hongkong, China.

Yaoyang Qiu (Y)

Canon Medical Systems (China) Co., LTD, Beijing, China.

Panjie Gou (P)

Canon Medical Systems (China) Co., LTD, Beijing, China.

Gongping Chen (G)

College of Artificial Intelligence, Nankai University, Tianjin, China.

Lei Zhao (L)

College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.

Jianxun Zhang (J)

College of Artificial Intelligence, Nankai University, Tianjin, China.

Yu Dai (Y)

College of Artificial Intelligence, Nankai University, Tianjin, China.

Fangyijie Wang (F)

School of Medicine, University College Dublin, Dublin, Ireland.

Guénolé Silvestre (G)

School of Medicine, University College Dublin, Dublin, Ireland.

Kathleen Curran (K)

School of Computer Science, University College Dublin, Dublin, Ireland.

Hongkun Sun (H)

School of Statistics & Mathematics, Zhejiang Gongshang University, Hangzhou, China.

Jing Xu (J)

School of Statistics & Mathematics, Zhejiang Gongshang University, Hangzhou, China.

Pengzhou Cai (P)

School of Computer Science & Engineering, Chongqing University of Technology, Chongqing, China.

Lu Jiang (L)

School of Computer Science & Engineering, Chongqing University of Technology, Chongqing, China.

Libin Lan (L)

School of Computer Science & Engineering, Chongqing University of Technology, Chongqing, China.

Dong Ni (D)

National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.

Mei Zhong (M)

NanFang Hospital of Southern Medical University, Guangzhou, China.

Gaowen Chen (G)

Zhujiang Hospital of Southern Medical University, Guangzhou, China.

Víctor M Campello (VM)

Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.

Yaosheng Lu (Y)

Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China.

Karim Lekadir (K)

Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

Classifications MeSH