Ultrasound (US) imaging is extensively utilized in obstetric assessments due to its non-invasive nature, absence of radiation, affordability, and capability for real-time imaging. US imaging offers a clear advantage over traditional vaginal digital examinations by providing immediate, detailed visual insights into labor progression, including cervical dilation, fetal orientation, descent of the fetal head, and the rate of labor progression. These details are critical for physicians to make informed decisions about delivery management, potentially reducing neonatal mortality rates. The process of intrapartum US imaging is typically segmented into five steps: scanning, identification of the standard plane, observation of structures, measurement of parameters, and diagnosis. Among these, identifying the standard plane is vital, as it must include all critical structures necessary for accurate parameter measurements, directly impacting labor evaluation.

For instance, the angle of progression (AoP) is an essential metric in assessing the cephalopelvic fit during labor. Accurate AoP measurement requires clear visualization of the longitudinal sagittal plane of the pubic symphysis (PS) and the fetal head in US images. Real-time AoP monitoring is instrumental in predicting the mode of delivery, guiding clinical interventions, and minimizing risks to both mother and infant. Consequently, accurate identification of the pubic symphysis-fetal head standard plane (PSFHSP), defined as the intrapartum US image that distinctly portrays the fetal head and PS structures, is crucial for precise AoP measurements.

This study presents an innovative approach to automatically recognizing the PSFHSP in ultrasound imaging. By compressing the depth and modifying the residual modules of the model, we have enhanced its classification precision. Unlike traditional deep learning models, which typically require significant computational resources and substantial energy consumption, our proposed model is optimized for real-time classification on resource-constrained and low-energy edge devices. This makes it particularly well-suited for use with portable, handheld intrapartum ultrasound machines, offering a significant advancement in the field by facilitating efficient and reliable diagnostic assessments in various clinical environments.

The whole dataset used for the PSFHS challenge of MICCAI2023 (https://ps-fh-aop-2023.grand-challenge.org/) includes two parts: one is this PSFHS dataset (https://doi.org/https://doi.org/10.5281/zenodo.10969427) and another is from the JNU-IFM dataset (https://doi.org/10.6084/m9.figshare.14371652). These images can also be used for the Intrapartum Ultrasound Grand Challenge (IUGC) 2024 of MICCAI 2024 (https://codalab.lisn.upsaclay.fr/competitions/18413). For transparency and reproducibility, the source code of our model has been made publicly accessible at https://github.com/Yaucleo/HPSSPNet.

Finally, we are delighted to share our work with the scientific community and domain experts in the prestigious journal,  Medical & Biological Engineering & Computing. We sincerely hope that this resource can provide valuable research groundwork and further insights for the community.

Reference

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