Abstract
Fetal biometric measurements are routinely done during pregnancy for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find standard planes to measure the fetal head, abdomen and femur. In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis to simultaneously localize, classify and measure the fetal body parts. We employ an attention mechanism with a stacked module to learn salient maps to suppress irrelevant ultrasound regions and efficient scan plane localization. We train on the fetal ultrasound video from routine examinations of 700 different patients. Our method called FetalNet outperforms existing state-of-the-art methods in both classification and segmentation in fetal ultrasound video recordings. The source code and pre-trained weights are publicly available (https://github.com/SanoScience/FetalNet).
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Acknowledgements
We would like to thank the following medical sonographers for data, annotations and clinical expertise: Jan Klasa, MD; Bogusław Marinković, MD; Wojciech Górczewski, MD; Norbert Majewski, MD; Anita Smal-Obarska, MD. This paper is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement Sano No 857533 and the International Research Agendas programme of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund and by Warsaw University of Technology (grant of the Scientific Discipline of Computer Science and Telecommunications agreement of 18/06/2020).
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Płotka, S., Włodarczyk, T., Klasa, A., Lipa, M., Sitek, A., Trzciński, T. (2021). FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_30
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