Spatial Overlap and Habitat Selection of Corvid Species in European Cities

Understanding habitat and spatial overlap in sympatric species of urban areas would aid in predicting species and community modifications in response to global change. Habitat overlap has been widely investigated for specialist species but neglected for generalists living in urban settings. Many corvid species are generalists and are adapted to urban areas. This work aimed to determine the urban habitat requirements and spatial overlap of five corvid species in sixteen European cities during the breeding season. All five studied corvid species had high overlap in their habitat selection while still having particular tendencies. We found three species, the Carrion/Hooded Crow, Rook, and Eurasian Magpie, selected open habitats. The Western Jackdaw avoided areas with bare soil cover, and the Eurasian Jay chose more forested areas. The species with similar habitat selection also had congruent spatial distributions. Our results indicate that although the corvids had some tendencies regarding habitat selection, as generalists, they still tolerated a wide range of urban habitats, which resulted in high overlap in their habitat niches and spatial distributions

Large premigratory roost of lesser kestrels (Falco naumanni) in Ioannina City, Greece: Trends, roost characteristics, and implications for conservation

[No abstract available

Weakly-supervised biomechanically-constrained CT/MRI registration of the spine.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched

Patient-specific virtual spine straightening andvertebra inpainting: An automatic framework forosteoplasty planning.

Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach. Firstly, the fractured spine is segmented with a three-step Convolution Neural Network (CNN) architecture. Next, a per-vertebra rigid registration to a healthy spine atlas restores its curvature. Finally, a GAN-based inpainting approach replaces the fractured vertebra with an estimation of its original shape. Based on this outcome, we then estimate the maximum amount of bone cement for injection. We evaluate our framework by comparing the virtual vertebrae volumes of ten patients to their healthy equivalent and report an average error of 3.88±7.63%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures