Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion.
We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC 27599, USA. niejingxin@gmail.com
Jingxin
J
Nie
Dinggang
D
Shen
D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D051379:Mice; D059906:Neuroimaging / Q000379:methods; D060388:Support Vector Machine