Deep transfer learning for MR image feature point descriptors

In order to solve the internal feature matching problem of nonlinear flexible biological tissues in MR images, This paper proposes a feature point descriptor generation model based on transfer learning and convolutional neural networks TBNet . Firstly, the Siamese network structure model is combined with transfer learning to obtain a pre-trained CNN model and then this paper proposes a batch-by-batch model fine-tuning strategy. Secondly, the extracted feature point descriptor is obtained using the fine-tuned model. Finally, Experiments show that the TBNet has higher robustness and accuracy than traditional SIFT, SURF and the state-of-the-art VGG16-based models.