ABSTRACT
The accuracy of placental maturity staging is important to the clinical diagnosis of small gestational age, stillbirth, and fetal death. Fetal viabilities, various gestational ages, and complicated imaging process have made placental maturity evaluation a tedious and time-consuming task. Despite development of numerous tools and techniques to access placental maturity, automatic placental maturity remains challenging. To address this issue, we proposed to automatically grade placental maturity by obtaining gray-scale features from B-mode ultrasound and vascular blood flow information from color Doppler energy images based on deep convolutional neural networks, such as AlexNet and VGGNet. The deep network can achieve high-level features to further improve the accuracy of placental maturity grading. Both the transfer learning strategy and unique data augmentation technique are utilized to further boost the recognition performance. Extensive experimental results demonstrate that our method achieves promising performance in placental maturity evaluation and would be beneficial in clinical application.
