Study on MRI Medical Image Segmentation Technology Based on CNN-CRF Model

Abstract

Image segmentation is an important technique for segmenting images without overlapping each other and having their own features. It has been rapidly developed in the field of medical imaging, but there is currently a difference between classification accuracy and segmentation accuracy for medical image segmentation. In this paper, the deep convolutional neural network is combined with the cascading structure, and a uniform learning framework is established with the use conditional random field. This paper first adds a cascading structure under the deep convolutional neural networks (DCNN) framework to more effectively simulate the direct dependencies between spatial closure tags. Secondly, the conditional random field (CRF) is used for post-segmentation processing, which effectively solves the contradiction between the segmentation accuracy and the network depth and the number of pooling times in the traditional convolutional network. Secondly, the CRF is used for post-segmentation processing, which effectively solves the contradiction between the segmentation accuracy and the network depth and the number of pooling times in the traditional convolutional network.