Abstract-Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T 2 -weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T 2 -weighted images of preterm infants acquired at 40 weeks PMA, axial T 1 -weighted images of ageing adults acquired at an average age of 70 years, and T 1 -weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol. Index Terms-Deep learning, convolutional neural networks, automatic image segmentation, preterm neonatal brain, adult brain, MRI.
Moeskops et al. REF applied a CNN model that used multiple patch sizes and various convolution kernel sizes to get multi-scale information of each voxel.
Automatic segmentation of MR brain images with a convolutional neural network
IEEE Transactions on Medical Imaging, 35(5), 1252-1261 (2016)
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