🧾 TL;DR 🧾¶

• Read this paper to find out more about the FH-PS-AOP dataset.

• This is a semantic segmentation challenge. The task is to segment fetal head (FH)-pubic symphysis (PS) and then compute angle of progression (AOP) based on the segmented FH-PS.

• Training data consisting of 4000 manually segmented cases is publically available on Zenodo. 

• Our challenge has two phases (check the timeline and instructions on how to prepare your algorithm submission):

  1. Preliminary Test Phase (401 test cases): Participants develop their methods and test them on a subset of the test set. 

  2. Final Test Phase (700 test cases): Participants submit their final methods and test them on a full test set. 

• NOTE: provided US images are not yet registered. We decided to leave this fundamental step to the participants as this can be an important methodological contribution that we did not want to bias in any way.

• ⏲ Timeline¶

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  ✅3 Jan 2022: Our paper describing the FH-PS-AOP dataset is published (Open Acess)📖

  ✅ 5 Jan 2023: Release of Public Training set (also called Set 1) on Zenodo🗂

  ✅ 30 April 2023: Accepting submissions to the Preliminary Test Phase 🤸‍♀️

  ✅  30 August 2023: Closing submissions to the Preliminary Test Phase 🛑

  ✅ 1 Sep 2023: Accepting submissions to the Final Test Phase🤸‍♂️

  ✅ 20 Sep 2023: Closing submissions to the Final Test Phase🛑

  ✅ 8 Oct 2023: 🎉Winners of the FH-PS-AOP challenge are publicly announced!🎉

  ✅ 1 April 2024: Release the entire dataset, however, users need to sign a data usage agreement, please contact bai_jieyun@126.com directly.

  ✅ 2 April 2024: Release the source codes of top 8 teams (https://github.com/maskoffs/PS-FH-MICCAI23)

🎯 Problem statement 🎯¶

FH-PS segmentation could further determine fetal head descent.¶

Current obstetric practice strives to avoid difficult vaginal deliveries and the clinician's skill resides mainly in precisely identifying the fetal head station at which forceps or ventouse should be applied. Digital transvaginal examination remains the ‘gold standard’ for obtaining this information, but it is a subjective evaluation with several limitations.

Recent studies have shown that transperineal ultrasound imaging in the mid-sagittal plane might allow objective quantification of the level of fetal head descent in the birth canal by measurement of measurements of angle of progression (AOP). AOP is the angle between a straight line drawn along the longitudinal axis of the pubic symphysis (PS) and a line drawn from the inferior edge of the PS to the leading edge of the fetal head (FH). FH-PS segmentation is a prerequisite for automatic estimation of parameters

Figure 1. Example of reference organ-at-risk (OAR) segmentations, displayed as color-coded three-dimensional binary masks.

👩‍🎓 The FH-PS-AOP Challenge👨‍🎓¶

The task of the FH-PS-AOP grand challenge is to automatically segment 700 FH-PSs from transperineal ultrasound images in the devised Set 2 (test set), given the availability of Set 1, consisting of 401 images.

Set 2 is held private and therefore not released to the potential participants to prevent algorithm tuning, but instead the algorithms have to be submitted in the form of Docker containers that will be run by organizers on Set 2. The challenge is organized by taking into account the current guidelines for biomedical image analysis competitions, in particular the recommendations of the Biomedical Image Analysis Challenges (BIAS) initiative for transparent challenge reporting.

🎇 Publication🎇¶

• Zhou M, Wang C, Lu Y, et al. The segmentation effect of style transfer on fetal head ultrasound image: a study of multi-source data. Med Biol Eng Comput. 2023;61(5):1017-1031. doi:10.1007/s11517-022-02747-1

  Ou, Z., Bai, J., Chen, Z., Lu, Y., Wang, H., Long, S., Chen, G., RTSeg-Net: A Lightweight Network for Real-time Segmentation of Fetal Head and Pubic Symphysis from Intrapartum Ultrasound Images. Computers in biology and medicine, 2024; 108501. doi:10.1016/j.compbiomed.2024.108501

  Qiu, R., Zhou M, Bai, J., Lu, Y., Wang, H. PSFHSP-Net: An Efficient Lightweight Network for Identifying Pubic Symphysis-Fetal Head Standard Plane from Intrapartum Ultrasound Images. Med Biol Eng Comput. 2024. https://doi.org/10.1007/s11517-024-03111-1

  Chen, Z. Ou, Y. Lu, J. Bai, Direction-guided and multi-scale feature screening for fetal head–pubic symphysis segmentation and angle of progression calculation, Expert Systems with Applications, 245 (2024) 123096. doi:10.1016/j.eswa.2023.123096

  Lu Y, Zhou M, Zhi D, et al. The JNU-IFM dataset for segmenting pubic symphysis-fetal head [published correction appears in Data Brief. 2022 Apr 01;42:108128]. Data Brief. 2022;41:107904. Published 2022 Feb 2. doi:10.1016/j.dib.2022.107904

  Lu Y, Zhi D, Zhou M, et al. Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression. Comput Math Methods Med. 2022;2022:5192338. Published 2022 Sep 2. doi:10.1155/2022/5192338

  Bai J, Sun Z, Yu S, 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. 2022;13:940150. Published 2022 Dec 2. doi:10.3389/fphys.2022.940150

  Chen G, Bai J, Ou Z, Lu Y, Wang H. PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head. Scientific Data. 2024. doi:10.1038/s41597-024-03266-4

  Chen, Y. Lu, S, Long, V, et al. Fetal Head and Pubic Symphysis Segmentation in Intrapartum Ultrasound Image Using a Dual-Path Boundary-Guided Residual Network.  IEEE Journal of Biomedical and Health Informatics. 2024