Keywords
Automatic glioma segmentation; Three dimensions; GrowCut; Bounding box; Reflectional symmetry
Abstract
Accurate segmentation of glioma from Magnetic Resonance (MR) imagery undoubtedly provides essential assistance for glioma resection and following progress evaluation after the resection. Numerous methods have been presented to segment glioma from Two Dimensional (2D) or Three Dimensional (3D) MR images. To deal with the complex structure of brain and the various shapes of glioma, methods based on selecting asymmetric areas with respect to the approximate symmetry of brain are widely used. This kind of methods, however, may fail in the case of segmenting the glioma across the mid-sagittal plane. This paper developed a fully 3D automatic asymmetry detection method for the glioma segmentation, while overcoming the location limitation in conventional asymmetry detection methods. The proposed 3D bounding box method locates glioma in the Voxel of Interest (VOI), which is checked and corrected by the reflectional method. With the accurate VOI, the improved 3D GrowCut method is employed to segment glioma automatically and quickly. We evaluated the accuracy of the proposed method by using both synthetic and real clinical MR image data. Experimental results show that our method conquers the difficulties of conventional asymmetry detection method when segmenting the glioma across the mid-sagittal plane successfully. Our method provides similar segmentation performance with manual segmentation and shows obvious higher efficiency and more convenience than 2D automatic segmentation method.
Citation
Wu G, Ji C, Yu J, Wang Y, Chen L, Shi Z, et al. Automatic Segmentation of Glioma from 3D MR Images by Using Location Free Asymmetry Detection. SM J Biomed Eng. 2017; 3(1): 1012.