Abstract
Angle of progress (AOP) is an important indicator used in assessing the progress of labor during delivery. However, manually measuring AOP is time consuming and subjective. In this study, we address the challenge of automatic AOP measurement of transperineal ultrasound (TPU) to achieve accurate monitoring of maternal and infant status. We propose a multitask framework for simultaneously locating the landmark of pubic symphysis endpoints and segmenting the region of the fetal head and pubic symphysis. We then exploit the localization of the landmarks to obtain the central axis of pubic symphysis. Afterward, we calculate the tangent of fetal head as it passes through the lower endpoint of pubic symphysis. Finally, we compute AOP from the central axis and tangent. Our framework is evaluated on the basis of a TPU dataset acquired at The First Affiliated Hospital of Jinan University, which is annotated by an ultrasound physician with over 10 years of experience. Our method achieves a mean difference of 7.6° and displays promising prospects for real-time monitoring of labor progress in clinical practice. To the best of our knowledge, this study is the first to apply deep learning methods to AOP measurements.
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Acknowledgement
This work was supported by the National Key Research and Development Project [2019YFC0120100, 2019YFC0121907].
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Zhou, M., Yuan, C., Chen, Z., Wang, C., Lu, Y. (2020). Automatic Angle of Progress Measurement of Intrapartum Transperineal Ultrasound Image with Deep Learning. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_39
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DOI: https://doi.org/10.1007/978-3-030-59725-2_39
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