Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker's appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil

Accurate Detection of 3D Tubular Tree Structures

Spatial resolution of Magnetic Resonance Angiography (MRA) makes it a powerful tool for diagnosis and surgical planning. However, image interpretation and visualization tools are missing, and three-dimensional measurements are not usually accessible [6]. Flexible visualization of the whole vascular tree and precise quantification of phenomenons like carotid stenosis are applications of an automated processing of MRA [5]. Building an accurate model of a tubular objects network such as bronchi or blood vessels can provide a substantial help for 3D visualization and quantification [4]. We presen

Predation risk in relation to brain size in alternative prey of pygmy owls varies depending on the abundance of main prey.

Large brains in prey may select for adoption of anti-predator behavior that facilitates escape. Prey species with relatively large brains have been shown to be less likely to fall prey to predators. This results in the prediction that individuals that have been captured by predators on average should have smaller brains than sympatric conspecifics. We exploited the fact that Eurasian pygmy owls Glaucidium passerinum hoard small mammals and birds in cavities and nest-boxes for over-winter survival, allowing for comparison of the phenotype of prey with that of live conspecifics. In Northern Europe, main prey of pygmy owls are voles of the genera Myodes and Microtus, while forest birds and shrews are the most important alternative prey. Large fluctuations (amplitude 100-200-fold) in vole populations induce rapid numerical responses of pygmy owls to main prey populations, which in turn results in varying predation pressure on small birds. We found, weighed and measured 153 birds in food-stores of pygmy owls and mist-netted, weighed and measured 333 live birds of 12 species in central-western Finland during two autumns with low (2017) and high (2018) pygmy owl predation risk. In two autumns, individuals with large brains were captured later compared to individuals with small brains, consistent with the hypothesis that such individuals survived for longer. Avian prey of pygmy owls had smaller heads than live birds in autumn 2018 when predation risk by pygmy owls was high. This difference in head size was not significant in 2017 when predation risk by pygmy owls was reduced. Finally, avian survivors were in better body condition than avian prey individuals. These findings are consistent with the hypothesis that pygmy owls differentially prey on birds in poor condition with small brains. These findings are consistent with the hypothesis that predation risk imposed by pygmy owls on small birds in boreal forests varies depending on the abundance of the main prey (voles)