Infant brain segmentation based on a combination of VGG-16 and U-Net deep neural networks

Abstract

Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre-processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty-four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg-2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.