3D Brain Tumor Image Segmentation Integrating Cascaded Anisotropic Fully Convolutional Neural Network and Hybrid Level Set Method

Abstract

Abstract The accuracy of three-dimensional (3D) brain tumor image segmentation is of great significance to brain tumor diagnosis. To enhance the accuracy of segmentation, this study proposes an algorithm integrating a cascaded anisotropic fully convolutional neural network (FCNN) and the hybrid level set method. The algorithm first performs bias field correction and gray value normalization on T1, T1C, T2, and fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images for preprocessing. It then uses a cascading mechanism to perform preliminary segmentation of whole tumors, tumor cores, and enhancing tumors by an anisotropic FCNN based on the relationships among the locations of the three types of tumor structures. This simplifies multiclass brain tumor image segmentation problems into three binary classification problems. At the same time, the anisotropic FCNN adopts dense connections and multiscale feature merging to further enhance performance. Model training is respectively conducted on the axial, coronal, and sagittal planes, and the segmentation results from the three different orthogonal views are combined. Finally, the hybrid level set method is adopted to refine the brain tumor boundaries in the preliminary segmentation results, thereby completing fine segmentation. The results indicate that the proposed algorithm can achieve 3D MRI brain tumor image segmentation of high accuracy and stability. Comparison of the whole-tumor, tumor-core, and enhancing-tumor segmentation results with the gold standards produced Dice similarity coefficients (Dice) of 0.9113, 0.8581, and 0.7976, respectively.