379 research outputs found
Estimation of Optical Aberrations in 3D Microscopic Bioimages
The quality of microscopy images often suffers from optical aberrations.
These aberrations and their associated point spread functions have to be
quantitatively estimated to restore aberrated images. The recent
state-of-the-art method PhaseNet, based on a convolutional neural network, can
quantify aberrations accurately but is limited to images of point light
sources, e.g. fluorescent beads. In this research, we describe an extension of
PhaseNet enabling its use on 3D images of biological samples. To this end, our
method incorporates object-specific information into the simulated images used
for training the network. Further, we add a Python-based restoration of images
via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with
the predicted PSF can not only remove the simulated aberrations but also
improve the quality of the real raw microscopic images with unknown residual
PSF. We provide code for fast and convenient prediction and correction of
aberrations.Comment: 7 pages, 9 figures, presented at ICFSP on 9 Sept 2022 in Paris,
France, to be published in ICFSP conference proceedings in IEEE Xplore
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Alignment Metric Accuracy
We propose a metric for the space of multiple sequence alignments that can be used to compare two alignments to each other. In the case where one of the alignments is a reference alignment, the resulting accuracy measure improves upon previous approaches, and provides a balanced assessment of the fidelity of both matches and gaps. Furthermore, in the case where a reference alignment is not available, we provide empirical evidence that the distance from an alignment produced by one program to predicted alignments from other programs can be used as a control for multiple alignment experiments. In particular, we show that low accuracy alignments can be effectively identified and discarded. We also show that in the case of pairwise sequence alignment, it is possible to find an alignment that maximizes the expected value of our accuracy measure. Unlike previous approaches based on expected accuracy alignment that tend to maximize sensitivity at the expense of specificity, our method is able to identify unalignable sequence, thereby increasing overall accuracy. In addition, the algorithm allows for control of the sensitivity/specificity tradeoff via the adjustment of a single parameter. These results are confirmed with simulation studies that show that unalignable regions can be distinguished from homologous, conserved sequences. Finally, we propose an extension of the pairwise alignment method to multiple alignment. Our method, which we call AMAP, outperforms existing protein sequence multiple alignment programs on benchmark datasets. A webserver and software downloads are available at http://bio.math.berkeley.edu/amap/
Efficient Algorithms for Moral Lineage Tracing
Lineage tracing, the joint segmentation and tracking of living cells as they
move and divide in a sequence of light microscopy images, is a challenging
task. Jug et al. have proposed a mathematical abstraction of this task, the
moral lineage tracing problem (MLTP), whose feasible solutions define both a
segmentation of every image and a lineage forest of cells. Their branch-and-cut
algorithm, however, is prone to many cuts and slow convergence for large
instances. To address this problem, we make three contributions: (i) we devise
the first efficient primal feasible local search algorithms for the MLTP, (ii)
we improve the branch-and-cut algorithm by separating tighter cutting planes
and by incorporating our primal algorithms, (iii) we show in experiments that
our algorithms find accurate solutions on the problem instances of Jug et al.
and scale to larger instances, leveraging moral lineage tracing to practical
significance.Comment: Accepted at ICCV 201
A platform for brain-wide imaging and reconstruction of individual neurons
The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain.Howard Hughes Medical InstitutePublished versio
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