Cerebrovascular Segmentation Algorithm Based on Focused MultiGaussians Model and Weighted 3D Markov Random Field Segmenting the cerebral vessels precisely from the timeofflight magnetic resonance angiography (TOFMRA) images is important for the diagnosis and therapy of the cerebrovascular diseases. Since the complex structures of cerebral vessels, the current cerebrovascular segmentation algorithms based on statistical model have less accuracy for stenotic vessels and are quite timeconsuming. In this paper, we propose a novel automatic cerebrovascular segmentation algorithm based on focused MultiGaussians (FMG) model and weighted 3D Markov Random Field. As far as our knowledge, this is the first time to adopt multiGaussians distributions as vascular model with the purpose of modeling the vascular tissue more accurately. Furthermore, the fitting range is narrowed to local region related to vessels in order to make the model focus on the vascular tissue and simplify the finite mixture model. To incorporate precise local character of images to the model, we design a new weighted 3D MRF by a weighted neighborhood system (WNBS). Finally, the particle swarm optimization (PSO) algorithm of parameter estimation has been implemented parallelly based on GPUs and the execution speed was improved by about 70 times. The experimental results show that the algorithm can produce detailed segmentation results especially for stenotic vessels.

Unsupervised Cerebrovascular Segmentation of TOFMRA Images Based on Deep Neural Network and Hidden Markov Random Field Model Automated cerebrovascular segmentation of timeofflight magnetic resonance angiography (TOFMRA) images can provide the quantitatively measuring method to evaluate the disorder of human cerebrovascular system. Deep neural network (DNN)based cerebrovascular segmentation methods have shown to give an outstanding performance, but they are limited by huge training dataset. In this paper, we proposed an unsupervised cerebrovascular segmentation method of TOFMRA images based on DNN and hidden Markov random field (HMRF) model. DNNbased cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested by 100 cases of TOFMRA images. The result was evaluated by dice similarity coefficient (DSC), which reached to 0.79. The trained model had a better performance than the traditional HMRFbased cerebrovascular segmentation method in binary pixelclassification. This paper merges advantages of both DNN and HMRF to address the limitation of amounts of annotations during deep learning training, which is a more effective cerebrovascular segmentation method.