CT Image Segmentation Using FEM with Optimized Boundary Condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery

Prion protein-specific antibodies that detect multiple TSE agents with high sensitivity

This paper describes the generation, characterisation and potential applications of a panel of novel anti-prion protein monoclonal antibodies (mAbs). The mAbs were generated by immunising PRNP null mice, using a variety of regimes, with a truncated form of recombinant ovine prion protein spanning residues 94–233. Epitopes of specific antibodies were mapped using solid-phase Pepscan analysis and clustered to four distinct regions within the PrP molecule. We have demonstrated the utility of these antibodies by use of Western blotting and immunohistochemistry in tissues from a range of different species affected by transmissible spongiform encephalopathy (TSE). In comparative tests against extensively-used and widely-published, commercially available antibodies, similar or improved results can be obtained using these new mAbs, specifically in terms of sensitivity of detection. Since many of these antibodies recognise native PrPC, they could also be applied to a broad range of immunoassays such as flow cytometry, DELFIA analysis or immunoprecipitation. We are using these reagents to increase our understanding of TSE pathogenesis and for use in potential diagnostic screening assays

A community-sourced glossary of open scholarship terms

Supplementary Information: This list of terms represents the ‘Open Scholarship Glossary 1.0’ (available at: https://forrt.org/glossary/. Glossary available under a CC BY NC SA 4.0 license at: https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pdf).https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pd

Identification of practically visible spatial objects in natural environments

Image retrieval of landscape photographs requires accurate annotation using multi-faceted descriptions relating to the subject and content of the photograph. The subject of such photographs is dominantly the terrain and spatial objects visible from the photographer’s viewpoint. While some spatial objects in the background may be obscured by foreground vegetation, other visible spatial objects beyond a certain distance may not present noteworthy elements of the captured scene (such as distant houses). Our aim is to assess approaches to improve the identification of practically visible spatial objects for image annotation. These approaches include the consideration of the apparent spatial object size and landcover information about occluding vegetation. These inputs are used to enhance viewshed analysis to accurately identify only spatial objects practically visible and therefore likely to be notable subjects of a photograph. The two approaches are evaluated in an experiment in a semi-rural area of Switzerland, whose results indicate that visual magnitude is key in accurate identification of visible spatial objects