ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
This addresses the challenging problem of skull stripping in neuroimaging for researchers and clinicians, though it appears incremental as it builds on existing techniques like ASM and CNN.
The paper tackled brain extraction (skull stripping) in MRI scans by proposing ASMCNN, a method combining Active Shape Model and Convolutional Neural Networks, which outperformed state-of-the-art algorithms in all experiments.
Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnetic Resonance Imaging (MRI) scans, namely ASMCNN, by combining the Active Shape Model (ASM) and Convolutional Neural Network (CNN) for taking full of their advantages to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in the sagittal plane. First, we divide images into different groups such that, in each group, shapes and structures of brain boundaries have similar appearances. Second, a modified version of ASM is used to detect brain boundaries by utilizing prior knowledge of each group. Finally, CNN and post-processing methods, including Conditional Random Field (CRF), Gaussian processes, and several special rules are applied to refine the segmentation contours. Experimental results show that our proposed method outperforms current state-of-the-art algorithms by a significant margin in all experiments.