This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

OBJECTIVE Evidence from prospective high-risk infant studies suggests that early symptoms of autism usually emerge late in the first- or early in the second-year of life after a period of relatively typical development. This is the first neuroimaging study to prospectively examine white matter fiber tract organization during this interval in infants who develop autism spectrum disorder (ASD) by 24 months. METHOD Participants included 92 infant siblings from an ongoing imaging study of autism. All participants had diffusion tensor imaging at 6 months and behavioral assessments at 24 months, with a majority contributing additional imaging data at either or both 12 and 24 months. At 24 months, 28 infants met criteria for ASD; 64 infants did not. Microstructural properties of white-matter fiber tracts reported to be associated with ASD or related behaviors were characterized by fractional anisotropy (FA) and radial and axial diffusivity. RESULTS FA trajectories differed significantly between infants who did versus did not develop ASD for 12 of 15 fiber tracts. Development for most fiber tracts in infants with ASD was characterized by elevated FA at 6 months followed by slower developmental change overtime relative to infants without ASD. Thus, by 24 months of age, lower FA values were evident for those with ASD. CONCLUSION These results suggest that the aberrant development of white matter pathways precede the manifestation of autistic symptoms in the first year of life. Longitudinal data are critical to characterizing the dynamic age-related brain and behavior changes underlying this neurodevelopmental disorder.

SUMMARY The hippocampus in schizophrenia is characterized by both hypermetabolism and reduced size. It remains unknown whether these abnormalities are mechanistically linked. Here, in addressing these questions we used MRI tools that can map hippocampal metabolism and structure in patients and mouse models. In at-risk patients, hypermetabolism was found to begin in CA1 and spread to the subiculum after psychosis onset. CA1 hypermetabolism at baseline predicted hippocampal atrophy, which occured during progression to psychosis, most prominently in similar regions. Next, we used ketamine to model conditions of acute psychosis in mice. Acute ketamine reproduced a regional pattern of hippocampal hypermetabolism, while repeated exposure shifted the hippocampus to a hypermetabolic state with concurrent atrophy and pathology in parvalbumin-expressing interneurons. Parallel in vivo experiments using LY379268 and direct measurements of extracellular glutamate showed that glutamate drives both neuroimaging abnormalities. These findings show that hippocampal hypermetabolism leads to atrophy in psychotic disorder and suggest glutamate as a pathogenic driver.

Large databases of high-resolution structural MR images are being assembled to quantitatively examine the relationships between brain anatomy, disease progression, treatment regimens, and genetic influences upon brain structure. Quantifying brain structures in such large databases cannot be practically accomplished by expert neuroanatomists using hand-tracing. Rather, this research will depend upon automated methods that reliably and accurately segment and quantify dozens of brain regions. At present, there is little guidance available to help clinical research groups in choosing such tools. Thus, our goal was to compare the performance of two popular and fully automated tools, FSL/FIRST and FreeSurfer, to expert hand tracing in the measurement of the hippocampus and amygdala. Volumes derived from each automated measurement were compared to hand tracing for percent volume overlap, percent volume difference, across-sample correlation, and 3-D group-level shape analysis. In addition, sample size estimates for conducting between-group studies were computed for a range of effect sizes. Compared to hand tracing, hippocampal measurements with FreeSurfer exhibited greater volume overlap, smaller volume difference, and higher correlation than FIRST, and sample size estimates with FreeSurfer were closer to hand tracing. Amygdala measurement with FreeSurfer was also more highly correlated to hand tracing than FIRST, but exhibited a greater volume difference than FIRST. Both techniques had comparable volume overlap and similar sample size estimates. Compared to hand tracing, a 3-D shape analysis of the hippocampus showed FreeSurfer was more accurate than FIRST, particularly in the head and tail. However, FIRST more accurately represented the amygdala shape than FreeSurfer, which inflated its anterior and posterior surfaces.

Objective Brain enlargement has been observed in 2 year old children with autism but the underlying mechanisms are unknown. This longitudinal MRI study investigated early growth trajectories in brain volume and cortical thickness. Method Cerebral gray and white matter volumes and cortical thickness in children with autism spectrum disorder and controls were examined. Subjects were seen at approximately 2 years of age (autism = 59, controls = 38) and were rescanned approximately 24 months later at age 4–5 years (autism = 38, controls = 21). Results We observed generalized cerebral cortical enlargement in individuals with ASD at both 2 and 4 – 5 years of age. Rate of cerebral cortical growth across multiple brain regions and tissue compartments, in individuals with ASD, was parallel to that seen in controls, indicating that there was no increase in rate of cerebral cortical growth during this interval. No cerebellar differences were observed in ASD. After controlling for TBV, a disproportionate enlargement in temporal lobe white matter was observed in the ASD group. We found no differences in cortical thickness, but an increase in an estimate of surface area in the ASD group compared to controls for all cortical regions measured (temporal, frontal, and parietal-occipital). Conclusions Our longitudinal MRI study found generalized cerebral cortical enlargement in children with ASD, with a disproportionate enlargement in temporal lobe white matter. There was no difference from controls in the rate of brain growth for this age interval, indicating brain enlargement in ASD results from an increased rate of brain growth prior to age 2. The presence of increased cortical volume, but not cortical thickness, suggests that early brain enlargement may be associated with increased cortical surface area. Cortical surface area overgrowth in ASD may underlie brain enlargement and implicates a distinct set of pathogenic mechanisms.