14,757 research outputs found
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a
transformed LiDAR point cloud as input and directly outputs a point-wise label
map, which is then refined by a conditional random field (CRF) implemented as a
recurrent layer. Instance-level labels are then obtained by conventional
clustering algorithms. Our CNN model is trained on LiDAR point clouds from the
KITTI dataset, and our point-wise segmentation labels are derived from 3D
bounding boxes from KITTI. To obtain extra training data, we built a LiDAR
simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize
large amounts of realistic training data. Our experiments show that SqueezeSeg
achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per
frame), highly desirable for autonomous driving applications. Furthermore,
additionally training on synthesized data boosts validation accuracy on
real-world data. Our source code and synthesized data will be open-sourced
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Global morphogenetic flow is accurately predicted by the spatial distribution of myosin motors.
During embryogenesis tissue layers undergo morphogenetic flow rearranging and folding into specific shapes. While developmental biology has identified key genes and local cellular processes, global coordination of tissue remodeling at the organ scale remains unclear. Here, we combine in toto light-sheet microscopy of the Drosophila embryo with quantitative analysis and physical modeling to relate cellular flow with the patterns of force generation during the gastrulation process. We find that the complex spatio-temporal flow pattern can be predicted from the measured meso-scale myosin density and anisotropy using a simple, effective viscous model of the tissue, achieving close to 90% accuracy with one time dependent and two constant parameters. Our analysis uncovers the importance of a) spatial modulation of myosin distribution on the scale of the embryo and b) the non-locality of its effect due to mechanical interaction of cells, demonstrating the need for the global perspective in the study of morphogenetic flow
Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform
Mechanical strain and stress play a major role in biological processes such
as wound healing or morphogenesis. To assess this role quantitatively, fixed or
live images of tissues are acquired at a cellular precision in large fields of
views. To exploit these data, large numbers of cells have to be analyzed to
extract cell shape anisotropy and cell size. Most frequently, this is performed
through detailed individual cell contour determination, using so-called
segmentation computer programs, complemented if necessary by manual detection
and error corrections. However, a coarse grained and faster technique can be
recommended in at least three situations. First, when detailed information on
individual cell contours is not required, for instance in studies which require
only coarse-grained average information on cell anisotropy. Second, as an
exploratory step to determine whether full segmentation can be potentially
useful. Third, when segmentation is too difficult, for instance due to poor
image quality or too large a cell number. We developed a user-friendly, Fourier
transform-based image analysis pipeline. It is fast (typically cells per
minute with a current laptop computer) and suitable for time, space or ensemble
averages. We validate it on one set of artificial images and on two sets of
fully segmented images, one from a Drosophila pupa and the other from a chicken
embryo; the pipeline results are robust. Perspectives include \textit{in vitro}
tissues, non-biological cellular patterns such as foams, and stacks.Comment: 13 pages; 9 figure
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