Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning.
Ultrasound Med Biol
; 48(4): 663-674, 2022 04.
Article
em En
| MEDLINE
| ID: mdl-35063289
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Placenta Prévia
/
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article