Abstract
OBJECTIVES: Previous studies on brain MRI image segmentation, such as threshold method, boundary detection method, and region method did not achieve good performance in complex scenes. Based on the deep learning segmentation technology, this study constructed a neural network model by using the algorithm of atrous convolution combined with conditional random field (CRF) to segment the thalamus, caudate nucleus, and lenticular nucleus in brain MRI, which laid a good foundation for MRI diagnosis of brain diseases. METHODS: A total of 1 200 MRI-Flair images of the brain were randomly selected, and 3 anatomical structures of thalamus, caudate nucleus, and lenticular nucleus were manually labeled, of which 1 000 were used as training data sets and 200 were used as test data sets. The neural network model was established by using deep convolutional neural networks (DCNN) combined with CRF algorithm. The training data set was input into the model, and the parameterized neural network model was obtained after iteration for 30 000 times. The test data set was used to evaluate, test, and output the predicted image. RESULTS: The model optimization results showed that the new brain MRI segmentation model DeepXAG had the highest accuracy. Therefore, DeepXAG was selected as the segmentation algorithm. The mean intersection over union (mIOU) of the DeepXAG model was 72.3%, which was significantly higher than other classical segmentation algorithms (CRF-RNN1, FCN-8s2, DPN3, RefineNet4, and PSPNet5). CONCLUSIONS: The DeepXAG algorithm has good accuracy and robustness in segmenting the anatomical structure of brain MRI images.