Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Characterization of Halyomorpha halys (brown marmorated stink bug) biogenic volatile organic compound emissions and their role in secondary organic aerosol formation.

The formation of aerosols is a key component in understanding cloud formation in the context of radiative forcings and global climate modeling. Biogenic volatile organic compounds (BVOCs) are a significant source of aerosols, yet there is still much to be learned about their structures, sources, and interactions. The aims of this project were to identify the BVOCs found in the defense chemicals of the brown marmorated stink bug Halymorpha halys and quantify them using gas chromatography-mass spectrometry (GC/MS) and test whether oxidation of these compounds by ozone-promoted aerosol and cloud seed formation. The bugs were tested under two conditions: agitation by asphyxiation and direct glandular exposure. Tridecane, 2(5H)-furanone 5-ethyl, and (E)-2-decenal were identified as the three most abundant compounds. H. halys were also tested in the agitated condition in a smog chamber. It was found that in the presence of 100-180 ppm ozone, secondary aerosols do form. A scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) were used to characterize the secondary aerosols that formed. This reaction resulted in 0.23 microg/ bug of particulate mass. It was also found that these secondary organic aerosol particles could act as cloud condensation nuclei. At a supersaturation of 1%, we found a kappa value of 0.09. Once regional populations of these stink bugs stabilize and the populations estimates can be made, the additional impacts of their contribution to regional air quality can be calculated.

Characterization of Halyomorpha Halys (Brown Marmorated Stink Bug) Biogenic Volatile Organic Compound Emissions and Their Role in Secondary Organic Aerosol Formation

The formation of aerosols is a key component in understanding cloud formation in the context of radiative forcings and global climate modeling. Biogenic volatile organic compounds (BVOCs) are a significant source of aerosols, yet there is still much to be learned about their structures, sources, and interactions. The aims of this project were to identify the BVOCs found in the defense chemicals of the brown marmorated stink bug Halymorpha halys and quantify them using gas chromatography-mass spectrometry (GC/MS) and test whether oxidation of these compounds by ozone-promoted aerosol and cloud seed formation. The bugs were tested under two conditions: agitation by asphyxiation and direct glandular exposure. Tridecane, 2(5H)-furanone 5-ethyl, and (E)-2-decenal were identified as the three most abundant compounds. H. halys were also tested in the agitated condition in a smog chamber. It was found that in the presence of 100-180 ppm ozone, secondary aerosols do form. A scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) were used to characterize the secondary aerosols that formed. This reaction resulted in 0.23 mu g/bug of particulate mass. It was also found that these secondary organic aerosol particles could act as cloud condensation nuclei. At a supersaturation of 1%, we found a kappa value of 0.09. Once regional populations of these stink bugs stablilize and the populations estimates can be made, the additional impacts of their contribution to regional air quality can be calculated. Implications: Halymorpha halys (brown marmorated stink bugs) are a relatively new invasive species introduced in the United States near Allentown, Pennsylvania. The authors chemically speciated the bugs' defense pheromones and found that tridecane, 5-ethyl-2(5H)-furanone, and (E)-2-decenal dominated their emissions. Their defense emissions were reacted with atmospherically relevant concentrations of ozone and resulted in 0.23 g of particulate matter per emission per bug. Due to the large population of these bugs in some regions, these emissions could contribute appreciably to a region's PM2.5 (particulate matter with an aerodynamic diameter 2.5 m) levels.

Automatic Scan Planning for Magnetic Resonance Imaging of the Knee Joint

Automatic scan planning for magnetic resonance imaging of the knee aims at defining an oriented bounding box around the knee joint from sparse scout images in order to choose the optimal field of view for the diagnostic images and limit acquisition time. We propose a fast and fully automatic method to perform this task based on the standard clinical scout imaging protocol. The method is based on sequential Chamfer matching of 2D scout feature images with a three-dimensional mean model of femur and tibia. Subsequently, the joint plane separating femur and tibia, which contains both menisci, can be automatically detected using an information-augmented active shape model on the diagnostic images. This can assist the clinicians in quickly defining slices with standardized and reproducible orientation, thus increasing diagnostic accuracy and also comparability of serial examinations. The method has been evaluated on 42 knee MR images. It has the potential to be incorporated into existing systems because it does not change the current acquisition protocol.

Electrophysiological and behavioral correlates of stable automatic semantic retrieval in aging

Previous studies have shown both declining and stable semantic-memory abilities during healthy aging. There is consistent evidence that semantic processes involving controlled mechanisms weaken with age. In contrast, results of aging studies on automatic semantic retrieval are often inconsistent, probably due to methodological limitations and differences. The present study therefore examines age-related alterations in automatic semantic retrieval and memory structure with a novel combination of critical methodological factors, i.e., the selection of subjects, a well-designed paradigm, and electrophysiological methods that result in unambiguous signal markers. Healthy young and elderly participants performed lexical decisions on visually presented word/non-word pairs with a stimulus onset asynchrony (SOA) of 150 ms. Behavioral and electrophysiological data were measured, and the N400-LPC complex, an event-related potential component sensitive to lexical-semantic retrieval, was analyzed by power and topographic distribution of electrical brain activity. Both age groups exhibited semantic priming (SP) and concreteness effects in behavioral reaction time and the electrophysiological N400-LPC complex. Importantly, elderly subjects did not differ significantly from the young in their lexical decision and SP performances as well as in the N400-LPC SP effect. The only difference was an age-related delay measured in the N400-LPC microstate. This could be attributed to existing age effects in controlled functions, as further supported by the replicated age difference in word fluency. The present results add new behavioral and neurophysiological evidence to earlier findings, by showing that automatic semantic retrieval remains stable in global signal strength and topographic distribution during healthy aging.

Pathology hinting as the combination of automatic segmentation with a statistical shape model

With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.