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Collagen fibers play an important role in the biomechanics of the blood vessel wall. The objective of this study was to determine the 3D microstructure of collagen fibers in the media and adventitia of coronary arteries. We present a novel optimal angle consistence algorithm to reform image slices in the visualization and analysis of 3D collagen images. 3D geometry was reconstructed from resliced image space where the 3D skeleton was extracted as the primary feature for accurate reconstruction of geometrical parameters. Collagen fibers (range 80–200) were reconstructed from the porcine coronary artery wall for the measurement of various morphological parameters. Collagen waviness and diameters were 1.37 ± 0.19 and 2.61 ± 0.89 μm, respectively. The biaxial distributions of orientation had two different peaks at 110.7 ± 25.2° and 18.4 ± 19.3°. Results for width, waviness, and orientation were found to be in good agreement with manual measurements. In addition to accurately measuring 2D features more efficiently than the manual approach, the present method produced 3D features that could not be measured in the 2D manual approach. These additional parameters included the tilt angle (5.10 ± 2.95°) and cross-sectional area (CSA; 5.98 ± 3.79 μm2) of collagen fibers. These 3D collagen reconstructions provide accurate and reliable microstructure for biomechanical modeling of vessel wall mechanics. [ABSTRACT FROM AUTHOR]

Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. In this pilot study, we present a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs’ behaviour was checked through a validation process. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals’ quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog’s shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed. [ABSTRACT FROM AUTHOR]