RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images

Ou, Zhanhong; Bai, Jieyun; Chen, Zhide; Lu, Yaosheng; Wang, Huijin; Long, Shun; Chen, Gaowen

doi:10.1016/j.compbiomed.2024.108501

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…This challenge drew over 100 teams to develop AI algorithms specifically for obstetric ultrasound imaging. The goal was not limited to analyzing images; it also encompassed the assurance that these AI solutions conform to clinical standards while assessing biometric parameters accurately (6,(22)(23)(24)(25). The triumph of the MICCAI-PSFHS Challenge underscores the evolution of advanced intrapartum ultrasound technology, highlighting not just technological progress but also the potential of AI models to predict the most suitable delivery methods.…”

Section: Intrapartum Ultrasound In Assessing Labor Dynamicsmentioning

confidence: 99%

“…However, significant challenges hinder the efficacy and affordability of existing interventions. As such, this synopsis aggregates the recent breakthroughs ( 5 ) and methodologies ( 6 – 11 ), delves into potential impact on improving maternal and infant safety, and reflects on how these advancements may guide future academic research.…”

Section: Introductionmentioning

confidence: 99%

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Editorial: New technologies improve maternal and newborn safety

Bai,

Lu,

Liu

et al. 2024

Front. Med. Technol.

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Section: Intrapartum Ultrasound In Assessing Labor Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Editorial: New technologies improve maternal and newborn safety

Bai,

Lu,

Liu

et al. 2024

Front. Med. Technol.

Self Cite

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Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning

Jiang,

Wang,

Bai

et al. 2024

Lecture Notes in Computer Science

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PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images

Qiu,

Zhou,

Bai

et al. 2024

Med Biol Eng Comput

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The accurate selection of the ultrasound plane for the fetal head and pubic symphysis is critical for precisely measuring the angle of progression. The traditional method depends heavily on sonographers manually selecting the imaging plane. This process is not only time-intensive and laborious but also prone to variability based on the clinicians’ expertise. Consequently, there is a significant need for an automated method driven by artificial intelligence. To enhance the efficiency and accuracy of identifying the pubic symphysis-fetal head standard plane (PSFHSP), we proposed a streamlined neural network, PSFHSP-Net, based on a modified version of ResNet-18. This network comprises a single convolutional layer and three residual blocks designed to mitigate noise interference and bolster feature extraction capabilities. The model’s adaptability was further refined by expanding the shared feature layer into task-specific layers. We assessed its performance against both traditional heavyweight and other lightweight models by evaluating metrics such as F1-score, accuracy (ACC), recall, precision, area under the ROC curve (AUC), model parameter count, and frames per second (FPS). The PSFHSP-Net recorded an ACC of 0.8995, an F1-score of 0.9075, a recall of 0.9191, and a precision of 0.9022. This model surpassed other heavyweight and lightweight models in these metrics. Notably, it featured the smallest model size (1.48 MB) and the highest processing speed (65.7909 FPS), meeting the real-time processing criterion of over 24 images per second. While the AUC of our model was 0.930, slightly lower than that of ResNet34 (0.935), it showed a marked improvement over ResNet-18 in testing, with increases in ACC and F1-score of 0.0435 and 0.0306, respectively. However, precision saw a slight decrease from 0.9184 to 0.9022, a reduction of 0.0162. Despite these trade-offs, the compression of the model significantly reduced its size from 42.64 to 1.48 MB and increased its inference speed by 4.4753 to 65.7909 FPS. The results confirm that the PSFHSP-Net is capable of swiftly and effectively identifying the PSFHSP, thereby facilitating accurate measurements of the angle of progression. This development represents a significant advancement in automating fetal imaging analysis, promising enhanced consistency and reduced operator dependency in clinical settings. Graphical abstract

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RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images

Cited by 5 publications

References 39 publications

Editorial: New technologies improve maternal and newborn safety

Editorial: New technologies improve maternal and newborn safety

Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning

PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images

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