Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning.

M. Schilpzand, C. Neff, J. van Dillen, B. van Ginneken, T. Heskes, C. de Korte and T. van den Heuvel




Placenta localization from obstetric 2-D ultrasound (US) imaging is unattainable for many pregnant women in low-income countries because of a severe shortage of trained sonographers. To address this problem, we present a method to automatically detect low-lying placenta or placenta previa from 2-D US imaging. Two-dimensional US data from 280 pregnant women were collected in Ethiopia using a standardized acquisition protocol and low-cost equipment. The detection method consists of two parts. First, 2-D US segmentation of the placenta is performed using a deep learning model with a U-Net architecture. Second, the segmentation is used to classify each placenta as either normal or a class including both low-lying placenta and placenta previa. The segmentation model was trained and tested on 6574 2-D US images, achieving a median test Dice coefficient of 0.84 (interquartile range = 0.23). The classifier achieved a sensitivity of 81% and a specificity of 82% on a holdout test set of 148 cases. Additionally, the model was found to segment in real time (19 ± 2 ms per 2-D US image) using a smartphone paired with a low-cost 2-D US device. This work illustrates the feasibility of using automated placenta localization in a resource-limited setting.