An Accurate Segmentation Method for Volumetry of Brain Tumor in 3D MRI

ABSTRACT Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3D) image segmentation technique. First, an expert radiologist identified the central location of a tumor. Then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3D image of the tumor into a two-dimensional (2D) image by use of a "spiral-scanning" technique, in which a group of radial lines originating from the center of the tumor scanned the 3D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial lines provided a transformed 2D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2D image. We then transformed the optimal outline back into 3D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and an expert radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%. Our proposed method is reliable and would be useful for the management of various brain tumors