Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

Optimizing automated preprocessing streams for brain morphometric comparisons across multiple primate species

INTRODUCTION

MR techniques have delivered images of brains from a wide array of species, ranging from invertebrates to birds to elephants and whales. However, their potential to serve as a basis for comparative brain morphometric investigations has rarely been tapped so far (Christidis and Cox, 2006; Van Essen & Dierker, 2007), which also hampers a deeper understanding of the mechanisms behind structural alterations in neurodevelopmental disorders (Kochunov et al., 2010). One of the reasons for this is the lack of computational tools suitable for morphometrci comparisons across multiple species. In this work, we aim to characterize this gap, taking primates as an example.

METHODS

Using a legacy dataset comprising MR scans from eleven species of haplorhine primates acquired on the same scanner (Rilling & Insel, 1998), we tested different automated processing streams, focusing on denoising and brain segmentation. Newer multi-species datasets are not currently available, so our experiments with this decade-old dataset (which had a very low signal-to-noise ratio by contemporary standards) can serve to highlight the lower boundary of the current possibilities of automated processing pipelines. After manual orientation into Talairach space, an automated bias correction was performed using CARET (Van Essen et al., 2001) before the brains were extracted with FSL BET (Smith, 2002; Fig. 1) and either smoothed by an isotropic Gaussian Kernel, FSL SUSAN (Smith, 1996), an anisotropic diffusion filter (Perona & Malik, 1990), an optimized Rician non-local means filter (Gaser & Coupé, 2010), or not at all (Fig. 2 & 3). Segmentation of the brains (Fig. 2 & 4) was performed separately by either FSL FAST (Zhang, 2001) without atlas priors, or using an Adaptive Maximum A Posteriori Approach (Rajapakse et al., 1997). Finally, the white matter surface was extracted with CARET, and inspected for anatomical and topological correctness. 

RESULTS

Figure 3 shows that noise reduction was generally necessary but, at least for these noisy data, anisotropic filtering (SUSAN, diffusion filter, Rician filter) provided little improvement over simple isotropic filtering. While several segmentations worked well in individual species, our focus was on cross-species optimization of the processing pipeline, and none of the tested segmentations performed uniformly well in all 11 species. The performance could be improved by some of the denoising approaches and by deviating systematically from the default parameters recommended for processing human brains (cf. Fig. 4). Depending on the size of the brains and on the processing path, it took a double-core 2.4GHz iMac from about two minutes (squirrel monkeys) to half an hour (humans) to generate the white matter surface from the T1 image. Nonetheless, the resulting surfaces always necessitated topology correction and - often considerable - manual cleanup. 


CONCLUSIONS

Automated processing pipelines for surface-based morphometry still require considerable adaptations to reach optimal performance across brains of multiple species, even within primates (cf. Fig. 5). However, most contemporary datasets have a better signal-to-noise ratio than the ones used here, which provides for better segmentations and cortical surface reconstructions. Considering further that cross-scanner variability is well below within-species differences (Stonnington, 2008), the prospects look good for comparative evolutionary analyses of cortical parameters, and gyrification in particular. In order to succeed, however, computational efforts on comparative morphometry depend on high-quality imaging data from multiple species being more widely available.

ACKNOWLEDGMENTS

D.M, R.D, & C.G are supported by the German BMBF grant 01EV0709.


REFERENCES

Christidis, P & Cox, RW (2006), A Step-by-Step Guide to Cortical Surface Modeling of the Nonhuman Primate Brain Using FreeSurfer, Proc Human Brain Mapping Annual Meeting, http://afni.nimh.nih.gov/sscc/posters/file.2006-06-01.4536526043 .
Gaser, C & Coupé, P (2010), Impact of Non-local Means filtering on Brain Tissue Segmentation, OHBM 2010, Abstract 1770.
Kochunov, P & al. (2010), Mapping primary gyrogenesis during fetal development in primate brains: high-resolution in utero structural MRI study of fetal brain development in pregnant baboons, Frontiers in Neurogenesis, in press, DOI: 10.3389/fnins.2010.00020.
Perona, P & Malik J (1990), Scale space and edge detection using anisotropic diffusion, IEEE Trans Pattern Anal Machine Intell, vol. 12, no. 7, pp. 629-639.
Rajapakse, JC & al. (1997), Statistical approach to segmentation of single-channel cerebral MR images, IEEE Trans Med Imaging, vol. 16, no. 2, pp. 176-186.
Rilling, JK & Insel TR (1998), Evolution of the cerebellum in primates: differences in relative volume among monkeys, apes and humans. Brain Behav. Evol. 52, 308-314 doi:10.1159/000006575. Dataset available at http://www.fmridc.org/f/fmridc/77.html .
Smith, SM (1996), Flexible filter neighbourhood designation, Proc. 13th Int. Conf. on Pattern Recognition, vol. 1, pp. 206-212.
Smith, SM (2002), Fast robust automated brain extraction, Hum Brain Mapp, vol. 17, no. 3, pp. 143-155.
Stonnington, CM & al. (2008), Interpreting scan data acquired from multiple scanners: a study with Alzheimers disease, Neuroimage, vol. 39, no. 3, pp. 1180-1185.
Van Essen, DC & al. (2001), An Integrated Software System for Surface-based Analyses of Cerebral Cortex, J Am Med Inform Assoc, vol. 8, no. 5, pp. 443-459.
Van Essen, DC & Dierker DL (2007), Surface-based and probabilistic atlases of primate cerebral cortex, Neuron, vol. 56, no. 2, pp. 209-225.
Zhang, Y & al. (2001), Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm, IEEE Trans Med Imaging, vol. 20, no. 1, pp. 45-57.
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An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results