Megapixel camera arrays for high-resolution animal tracking in multiwell plates

Tracking small laboratory animals such as flies, fish, and worms is used for phenotyping in neuroscience, genetics, disease modelling, and drug discovery. Current imaging systems are limited either in spatial resolution or throughput. A system capable of imaging a large number of animals with sufficient resolution to estimate their pose would enable a new class of experiments where detailed behavioural differences are quantified but at a scale where hundreds of treatments can be tested simultaneously. Here we report a new imaging system consisting of an array of six 12-megapixel cameras that can simultaneously record from all the wells of a 96-well plate with a resolution of 80 pixels/mm at 25 frames per second. We show that this resolution is sufficient to estimate the pose of nematode worms including head identification and to extract high-dimensional phenotypic fingerprints. We illustrate the potential application of the system in three domains with a study of behavioural variability across wild isolates, experiments on the escape response and sensitisation of worms to repeated blue light stimulation, and the utility of this stimulus for phenotyping disease models. Because the system is compatible with standard multiwell plates, it makes computational ethological approaches accessible in existing high-throughput pipelines and greatly increases the scale of possible phenotypic screening experiments in C. elegans.

LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals

Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronised cameras per keypoint and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here, we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D’s versatility by applying it to multiple experimental systems using flies, mice, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded keypoints in freely behaving animals.

Refocusing Algorithm Based on Nonlinear SAI System

Synthetic aperture imaging (SAI) technology gets the light field information of the scene through the camera array. With the large virtual aperture, it can effectively acquire the information of the partially occluded object in the scene, and then we can focus on the arbitrary target plane corresponding to the reference perspective through the refocus algorithm. Meanwhile, objects that deviate from the plane will be blurred to varying degrees. However, when the object to be reconstructed in the scene is occluded by the complex foreground, the optical field information of the target cannot be effectively detected due to the limitation of the linear array. In order to deal with these problems, this paper proposes a nonlinear SAI method. This method can obtain the occluded object’s light field information reliably by using the nonlinear array. Experiments are designed for the nonlinear SAI, and refocusing is performed for the occluded objects with different camera arrays, different depths, and different distribution intervals. The results demonstrate that the method proposed in this paper is advanced than the traditional SAI method based on linear array.

Pre-eradication assessment of feral cat density and population size across Kangaroo Island, South Australia

Context Feral cats (Felis catus) are a significant threat to wildlife in Australia and globally. In Australia, densities of feral cats vary across the continent and also between the mainland and offshore islands. Densities on small islands may be at least an order of magnitude higher than those in adjacent mainland areas. To provide cat-free havens for biodiversity, cat-control and eradication programs are increasingly occurring on Australian offshore islands. However, planning such eradications is difficult, particularly on large islands where cat densities could vary considerably. Aims In the present study, we examined how feral cat densities vary among three habitats on Kangaroo Island, a large Australian offshore island for which feral cat eradication is planned. Methods Densities were compared among the following three broad habitat types: forest, forest–farmland boundaries and farmland. To detect cats, three remote-camera arrays were deployed in each habitat type, and density around each array was calculated using a spatially explicit capture–recapture framework. Key results The average feral cat density on Kangaroo Island (0.37 cats km−2) was slightly higher than that on the Australian mainland. Densities varied from 0.06 to 3.27 cats km−2 and were inconsistent within broad habitat types. Densities were highest on farms that had a high availability of macropod and sheep carcasses. The relationship between cat density and the proportion of cleared land in the surrounding area was weak. The total feral cat population of Kangaroo Island was estimated at 1629±661 (mean±s.e.) individuals. Conclusions Cat densities on Kangaroo Island are highly variable and may be locally affected by factors such as prey and carrion availability. Implications For cat eradication to be successful, resources must be sufficient to control at least the average cat density (0.37 cats km−2), with additional effort around areas of high carcass availability (where cats are likely to be at a higher density) potentially also being required.