Segmentation of rodent brains from MRI based on a novel statistical structure prediction method
Jinghao Zhou, Sukmoon Chang, Shaoting Zhang, George Pappas, Michael Michaelides, Foteini Delis, Nora D. Volkow, Panayotis K. Thanos, Dimitris Metaxas · 2009
Functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneration. Although various segmentation methods in clinical studies have been proposed, most of them require a priori knowledge about the locations of the structures of interest, preventing the fully automatic segmentation. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic functional segmentation of 2D rodent brain MR images. The presented method focuses on detecting the topological changes of brain structures based on a novel area ratio criteria. The mean successful rate of the detection method shows 89.4% accuracy compared to the expert-identified ground truth.