Study of cerebral asymmetry in schizophrenia based on an novel automatic cerebral hemisphere segmentation method for MRI

Event Abstract Back to Event Study of cerebral asymmetry in schizophrenia based on an novel automatic cerebral hemisphere segmentation method for MRI Lu Zhao1*, Ulla Ruotsalainen1, Jussi Hirvonen2, Jarmo Hietala3 and Jussi Tohka1 1 Tampere University of Technology, Finland 2 Turku PET Centre, Finland 3 University of Turku, Finland In this work, we statistically analyzed the cerebral volume asymmetry for schizophrenic and healthy populations by employing a novel and completely automatic cerebral hemisphere segmentation method for MRI. The segmentation method is based on the information of partial volume effect (PVE) and the partial differential equations based shape bottlenecks (PDE-SB) algorithm [1]. PVE is an inevitable problem of MRI, but it is rarely taken into account by the existing automatic MR brain image segmentation techniques. The PDE-SB algorithm can detect the bottlenecks connecting two parts of a complex object by simulating a steady state of the information transmission process between the two parts with partial differential equations. Our method consists of two major stages: compartmental brain decomposition [2] and cerebral hemisphere segmentation [3]. Firstly, a partial volume estimation approach [4] is used to obtain the partial volume voxel classification [into grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), CSF/GM, GM/WM and CSF/background] and estimate the amount of each tissue type in each brain voxel. Then the WM+GM/WM region is split into compartmental seeds corresponding to cerebrum, cerebellum and brainstem utilizing the PDE-SB algorithm. The brain volume is compartmentally decomposed by reconstructing the original shapes of the three compartments from the compartmental seeds according to the compartmental boundary knowledge defined with partial volume information. The cerebrum portion is extracted from the decomposed brain volume, and the cerebral hemispheres are segmented with the PDE-SB algorithm and the partial volume estimation results. In this work, this method was applied to a set of clinical T1-weighted MR images of 18 schizophrenic (11 males, 7 females) and 19 healthy (12 males, 7 females) subjects. All subjects were right-handed, and the schizophrenic patients were never medicated. With the obtained PVE information and cerebral hemisphere segmentation, the asymmetry index (AI) was calculated for cerebrum, cerebral GM and cerebral WM. AI was defined as (VR-VL)/[1/2(VR+VL)]×100, where VR and VL were the volume sizes of the right and left hemispheres respectively. The permutation test was conducted to evaluate the statistical significance of the difference of acquired AIs between schizophrenic and healthy populations. Here, the null hypothesis was defined as that AIs of schizophrenic and healthy populations had identical probability distribution, and the significance level was selected as 0.05. According to the statistical analysis results, the cerebral GM volume asymmetry of female schizophrenic population had significantly different probability distribution from the female healthy population's (p=0.002). There were no significant difference between schizophrenic and healthy populations with regard to volume asymmetry in the other cases (p>0.05). Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Poster Presentation Topic: Neuroimaging Citation: Zhao L, Ruotsalainen U, Hirvonen J, Hietala J and Tohka J (2008). Study of cerebral asymmetry in schizophrenia based on an novel automatic cerebral hemisphere segmentation method for MRI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.069 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Lu Zhao, Tampere University of Technology, Tampere, Finland, lu.zhao@tut.fi Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Google Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Google Scholar Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka PubMed Lu Zhao Ulla Ruotsalainen Jussi Hirvonen Jarmo Hietala Jussi Tohka Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.