TY - GEN
T1 - Automatic ultrasound assessment of placenta previa during the third trimester for rural areas
AU - Saavedra, Ana Cecilia
AU - Arroyo, Junior
AU - Tamayo, Lorena
AU - Egoavil, Miguel
AU - Ramos, Berta
AU - Castaneda, Benjamin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - Placenta previa describes the abnormal implantation of the placenta in the uterus of pregnant women, entirely or partially obstructing the internal cervical os. For clinical significance, it is suggested to assess the final placental location in the third trimester. In rural areas of developing countries, the absence of radiologists and sophisticated ultrasound systems make difficult the diagnosis of this condition. Ten pregnant women in their third trimester (age range of 22-43 years) were enrolled in this study. A volume sweep imaging obstetric protocol based on eight free-hand sweeping (three transverse and five longitudinal throughout the maternal abdominal area) was used. The acquisition was performed using a portable ultrasound scanner by a non-specialist. The dataset consisted of eight videos per volunteer and one hundred frames per video on average. Placental segmentation was carried out using the U-Net deep learning architecture and the leave one out cross-validation approach. The output masks combined with the knowledge of the acquisition protocol allowed to assess the spatial location likelihood of the placenta in the uterus, which is represented by a heat map. The method showed a sensitivity of 75% and a specificity of 92 %. Albeit the need for further validation, this approach may automatically warn of possible risks in childbirth, giving the chance to notify the specialist.
AB - Placenta previa describes the abnormal implantation of the placenta in the uterus of pregnant women, entirely or partially obstructing the internal cervical os. For clinical significance, it is suggested to assess the final placental location in the third trimester. In rural areas of developing countries, the absence of radiologists and sophisticated ultrasound systems make difficult the diagnosis of this condition. Ten pregnant women in their third trimester (age range of 22-43 years) were enrolled in this study. A volume sweep imaging obstetric protocol based on eight free-hand sweeping (three transverse and five longitudinal throughout the maternal abdominal area) was used. The acquisition was performed using a portable ultrasound scanner by a non-specialist. The dataset consisted of eight videos per volunteer and one hundred frames per video on average. Placental segmentation was carried out using the U-Net deep learning architecture and the leave one out cross-validation approach. The output masks combined with the knowledge of the acquisition protocol allowed to assess the spatial location likelihood of the placenta in the uterus, which is represented by a heat map. The method showed a sensitivity of 75% and a specificity of 92 %. Albeit the need for further validation, this approach may automatically warn of possible risks in childbirth, giving the chance to notify the specialist.
KW - Deep learning
KW - Fetus
KW - Placenta detection
KW - U-Net application
KW - Ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=85097865682&partnerID=8YFLogxK
U2 - 10.1109/IUS46767.2020.9251764
DO - 10.1109/IUS46767.2020.9251764
M3 - Conference contribution
AN - SCOPUS:85097865682
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2020 - International Ultrasonics Symposium, Proceedings
PB - IEEE Computer Society
T2 - 2020 IEEE International Ultrasonics Symposium, IUS 2020
Y2 - 7 September 2020 through 11 September 2020
ER -