Abstract
Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based cerebrovascular segmentation methods have shown to yield outstanding performance, they are limited by their dependence on huge training dataset. In this paper, we propose an unsupervised cerebrovascular segmentation method of TOF-MRA images based on DNN and hidden Markov random field (HMRF) model. Our DNN-based cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested using 100 TOF-MRA images. The results were evaluated using the dice similarity coefficient (DSC), which reached a value of 0.79. The trained model achieved better performance than that of the traditional HMRF-based cerebrovascular segmentation method in binary pixel-classification. This paper combines the advantages of both DNN and HMRF to train the model with a not so large amount of the annotations in deep learning, which leads to a more effective cerebrovascular segmentation method.
Highlights
According to the World Health Organization (WHO) report on the global burden of stroke, adult stroke mortality rate has reached 39% (Kim and Johnston, 2011)
A drawback of the abovementioned statistical model-based methods is that their segmentation performances significantly depend on the adaptation between statistical model and intensity histogram of MR images, and their performances are sensitive to the intensity distortion of time-of-flight magnetic resonance angiography (TOF-MRA) images
We evaluated the performance of Hidden Markov Random Field (HMRF) + deep neural network (DNN) framework in cerebrovascular segmentation to compare segmentation results using HMRF, HMRF + SegNet2D, and HMRF + U-Net3D methods
Summary
According to the World Health Organization (WHO) report on the global burden of stroke, adult stroke mortality rate has reached 39% (Kim and Johnston, 2011). Statistical modelbased methods extract cerebrovascular trees by fitting intensity distributions of different tissues into statistical models such as Gaussian mixture models. Hassouna et al (2006) proposed a 3D cerebrovascular segmentation method using stochastic models, which described the intensity histogram of MRA images by a finite mixture model consisting of one Rayleigh and two normal distributions. These stochastics models estimated spatial contextual information using 3D HMRF, they segmented blood vessels by optimizing HMRF and EM framework (Hassouna et al, 2006). A drawback of the abovementioned statistical model-based methods is that their segmentation performances significantly depend on the adaptation between statistical model and intensity histogram of MR images, and their performances are sensitive to the intensity distortion of TOF-MRA images
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