AUTOMATIC PLACENTA LOCALIZATION FROM ULTRASOUND IMAGING IN A RESOURCE-LIMITED SETTING USING A PREDEFINED ULTRASOUND ACQUISITION PROTOCOL AND DEEP LEARNING

Placenta ocalization 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. Twodimensional 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. (C) 2021 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology.

Automated summary

This study presents a method to automatically detect low-lying placenta or placenta previa from 2-D ultrasound imaging using a deep learning model. The method achieved high accuracy in placenta segmentation and classification, and showed feasibility in resource-limited settings.