Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Automated cement segmentation in vertebroplasty

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

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.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Is hatchery stocking a help or harm? Evidence, limitations and future directions in ecological and genetic surveys

Hatchery fish stocking for stock enhancement has been operated at a massive and global scale. However, the use of hatchery fish as a means of stock enhancement is highly controversial, and little is known about its effects on wild stock and consequences for stock enhancement. Here we review the scientific literature on this subject in order to address a fundamental - question is hatchery stocking a help or harm for wild stock and stock enhancement? We summarized 266 peer-reviewed papers that were published in the last 50 years, which describe empirical case studies on ecology and genetics of hatchery stocks and their effects on stock enhancement. Specifically, we asked whether hatchery stock and wild stock differed in fitness and the level of genetic variation, and whether stocking affected population abundance. Seventy studies contained comparisons between hatchery and wild stocks, out of which 23 studies showed significantly negative effects of hatchery rearing on the fitness of stocked fish, and 28 studies showed reduced genetic variation in hatchery populations. None of these studies suggested a positive genetic effect on the fitness of hatchery-reared individuals after release. These results suggest that negative effects of hatchery rearing are not just a concern but undeniably present in many aquaculture species. In a few cases, however, no obvious effect of hatchery rearing was observed, and a positive contribution of hatchery stock to the abundance of fish populations was indicated. These examples suggest that there is a chance to improve hatchery practices and mitigate the negative effects on wild stocks, although scientific data supporting the positive effect on stock enhancement are largely missing at this moment. Technically, microsatellite-based parentage assignments have been proven as a useful tool for the evaluation of reproductive fitness in natural settings, which is a key for stock enhancement by hatchery-based stocking. We discuss implications of these results, as well as their limitations and future directions. (C) 2010 Elsevier B.V. All rights reserved.

Occurrence, finger printing and ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in the Chenab River, Pakistan

Seventeen polycyclic aromatic hydrocarbons (PAHs) were studied in surface waters (including particulate phase) from the Chenab River, Pakistan and ranged from 289-994 and 437-1290 ng l-1 in summer and winter (2007-09), respectively. Concentrations for different ring-number PAHs followed the trend: 3-rings > 2-rings > 4-rings > 5-rings > 6-rings. The possible sources of PAHs are identified by calculating the indicative ratios; appropriating petrogenic sources of PAHs in urban and sub-urban regions with pyrogenic sources in agricultural region. Factor analysis based on principal component analysis identified the origins of PAHs from industrial activities, coal and trash burning in agricultural areas and municipal waste disposal from surrounding urban and sub-urban areas via open drains into the riverine ecosystem. Water quality guidelines and toxic equivalent factors highlighted the potential risk of low molecular weight PAHs to the aquatic life of the Chenab River. The flux estimated for PAHs contaminants from the Chenab River to the Indus River was >50 tons/year.

Thermal adaptation and ecological speciation

Ecological speciation is defined as the emergence of reproductive isolation as a direct or indirect consequence of divergent ecological adaptation. Several empirical examples of ecological speciation have been reported in the literature which very often involve adaptation to biotic resources. In this review, we investigate whether adaptation to different thermal habitats could also promote speciation and try to assess the importance of such processes in nature. Our survey of the literature identified 16 animal and plant systems where divergent thermal adaptation may underlie (partial) reproductive isolation between populations or may allow the stable coexistence of sibling taxa. In many of the systems, the differentially adapted populations have a parapatric distribution along an environmental gradient. Isolation often involves extrinsic selection against locally maladapted parental or hybrid genotypes, and additional pre- or postzygotic barriers may be important. Together, the identified examples strongly suggest that divergent selection between thermal environments is often strong enough to maintain a bimodal genotype distribution upon secondary contact. What is less clear from the available data is whether it can also be strong enough to allow ecological speciation in the face of gene flow through reinforcement-like processes. It is possible that intrinsic features of thermal gradients or the genetic basis of thermal adaptation make such reinforcement-like processes unlikely but it is equally possible that pertinent systems are understudied. Overall, our literature survey highlights (once again) the dearth of studies that investigate similar incipient species along the continuum from initial divergence to full reproductive isolation and studies that investigate all possible reproductive barriers in a given system.

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.