A prior feature SVM-MRF based method for mouse brain segmentation

Abstract

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.

Fig. 1

Convergence of the ICM algorithm with w₁=0.89 and w₂=0.11.

Fig. 2

Increased segmentation accuracy was obtained for the higher resolution, actively stained sets, relative to the formalin fixed sets, particularly in smaller structures like the anterior commissure (ac: from 50.76% to 83.5%), corpus callosum (cc: 65.59% to 85.44%), substantia nigra (SN: 78.36 to 91.55%) and ventricles (VS: 72.56 to 81.72%). For hippocampus and caudate putamen the values are more similar (~87% for Hc, and increased from 87.67 to 90.87 % for CPu).

Fig. 3

Visual assessment of comparable coronal levels through the brains C57BL/6, BXD29 and APP/TTA mouse model of AD, overlaid with automatically generated labels. The labeled regions are: anterior commisure (ac), corpus callosum (cc), caudate putamen (CPu), hippocampus (Hc), susbtantia nigra (SN) and the ventricular system (VS).

Fig. 4

Segmenting strains other than the one used for generating the priors (C57BL/6) is a more challenging task, as illustrated by the examples of a BXD29 and an APP/TTA mouse model of AD. Using a full sampling strategy, but only a subset of 7 labels, yields VOP for hippocampus, ranging from 94.11±0.73% in the C57BL/6 (for the 5 specimens) to 86.65% for the BXD29 and 84.97% for APP/TTA mouse. For the caudate putamen VOP ranges from 92.21±0.71% for C57BL6, to 87.68% for BXD29 and 79.28% for APP/TTA. However smaller white matter tracts and nuclei, and especially the ventricles remain challenging for automated segmentation (eg. VOP for corpus callosum 86.11±2.12% in C57BL/6, 55.25% in BXD29, and 63.83% in APP/TTA).