Your browser doesn't support javascript.
loading
Prenatal prediction and typing of placental invasion using MRI deep and radiomic features.
Xuan, Rongrong; Li, Tao; Wang, Yutao; Xu, Jian; Jin, Wei.
Afiliação
  • Xuan R; Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, Zhejiang, China.
  • Li T; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
  • Wang Y; Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, Zhejiang, China.
  • Xu J; Ningbo Women's and Children's Hospital, Ningbo, 315012, Zhejiang, China.
  • Jin W; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China. xyjw1969@126.com.
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.
Assuntos
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

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