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Combining automated image analysis with obstetric sweeps for prenatal ultrasound imaging in developing countries

: Heuvel, T.L.A. van den; Petros, H.; Santini, S.; Korte, C.L. de; Ginneken, B. van


Cardoso, M.J.:
Imaging for patient-customized simulations and systems for point-of-care ultrasound : International workshops, BIVPCS 2017 and POCUS 2017, held in conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017; Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10549)
ISBN: 978-3-319-67551-0 (Print)
ISBN: 978-3-319-67552-7 (Online)
ISBN: 3-319-67551-6
International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations (BIVPCS) <2017, Quèbec>
International Workshop on Point-of-Care Ultrasound - Algorithms, Hardware, and Applications (POCUS) <2017, Quèbec>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <20, 2017, Québec>
Fraunhofer MEVIS ()

Ultrasound imaging can be used to detect maternal risk factors, but it remains out of reach for most pregnant women in developing countries because there is a severe shortage of well-trained sonographers. In this paper we show the potential of combining the obstetric sweep protocol (OSP) with image analysis to automatically obtain information about the fetus. The OSP can be taught to any health care worker without any prior knowledge of ultrasound within a day, obviating the need for a well-trained sonographer to acquire the ultrasound images. The OSP was acquired from 317 pregnant women using a low-cost ultrasound device in St. Luke’s Hospital in Wolisso, Ethiopia. A deep learning network was used to automatically detect the fetal head in the OSP data. The fetal head detection was used to detect twins, determine fetal presentation and estimate gestational age without the need of a well-trained sonographer.