Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction

MRI-based fetal brain age prediction is crucial for fetal brain development analysis and early diagnosis of congenital anomalies. The locations and directions of fetal brain are randomly variable and disturbed by adjacent organs, thus imposing great challenges to the fetal brain age prediction. To address this problem, we propose an effective framework based on a deformable convolutional neural network for fetal brain age prediction. Considering the fact of insufficient data, we introduce label distribution learning (LDL), which is able to deal with the small sample problem. We integrate the LDL information into our end-to-end network. Moreover, to fully utilize the complementary multi-view data of fetal brain MRI stacks, a multi-branch CNN is proposed to aggregate multi-view information. We evaluate our method on a fetal brain MRI dataset with 289 subjects and achieve promising age prediction performance.