Prenatal prediction and typing of placental invasion using MRI deep and radiomic features.
Biomed Eng Online
; 20(1): 56, 2021 Jun 05.
Article
em En
| MEDLINE
| ID: mdl-34090428
ABSTRACT
BACKGROUND:
To predict placental invasion (PI) and determine the subtype according to the degree of implantation, and to help physicians develop appropriate therapeutic measures, a prenatal prediction and typing of placental invasion method using MRI deep and radiomic features were proposed.METHODS:
The placental tissue of abdominal magnetic resonance (MR) image was segmented to form the regions of interest (ROI) using U-net. The radiomic features were subsequently extracted from ROI. Simultaneously, a deep dynamic convolution neural network (DDCNN) with codec structure was established, which was trained by an autoencoder model to extract the deep features from ROI. Finally, combining the radiomic features and deep features, a classifier based on the multi-layer perceptron model was designed. The classifier was trained to predict prenatal placental invasion as well as determine the invasion subtype.RESULTS:
The experimental results show that the average accuracy, sensitivity, and specificity of the proposed method are 0.877, 0.857, and 0.954 respectively, and the area under the ROC curve (AUC) is 0.904, which outperforms the traditional radiomic based auxiliary diagnostic methods.CONCLUSIONS:
This work not only labeled the placental tissue of MR image in pregnant women automatically but also realized the objective evaluation of placental invasion, thus providing a new approach for the prenatal diagnosis of placental invasion.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
Biomed Eng Online
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China