Hybrid descriptor for placental maturity grading

Placental maturity grading (PMG) is quite essential to assess fetal growth and maternal health. To this date, PMG has mostly relied on the subjective judgment of the clinician, which is time-consuming and may cause wrong estimation due to redundancy and repeatability of the process. To tackle it, we propose an automatic method to stage placental maturity via deep hybrid descriptors based on B-mode ultrasound (BUS) and color Doppler energy (CDE) images. Specifically, convolutional descriptors extracted from multiple deep convolutional neural networks (DCNNs) and hand-crafted features are integrated to get the hybrid descriptors for grading performance boosting. First, different models with various feature layers are combined to obtain hybrid descriptors from images. Second, the transfer learning strategy is also utilized to enhance the grading performance via the deeply represented features. Third, extracted descriptors are encoded by Fisher vector (FV). Finally, we use support vector machine (SVM) as the classifier to grade placental maturity. The experimental results demonstrate that our proposed method could achieve good performance in PMG.