17,870 research outputs found
Microscopy Cell Segmentation via Adversarial Neural Networks
We present a novel method for cell segmentation in microscopy images which is
inspired by the Generative Adversarial Neural Network (GAN) approach. Our
framework is built on a pair of two competitive artificial neural networks,
with a unique architecture, termed Rib Cage, which are trained simultaneously
and together define a min-max game resulting in an accurate segmentation of a
given image. Our approach has two main strengths, similar to the GAN, the
method does not require a formulation of a loss function for the optimization
process. This allows training on a limited amount of annotated data in a weakly
supervised manner. Promising segmentation results on real fluorescent
microscopy data are presented. The code is freely available at:
https://github.com/arbellea/DeepCellSeg.gitComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI)
201
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Computational illumination for high-speed in vitro Fourier ptychographic microscopy
We demonstrate a new computational illumination technique that achieves large
space-bandwidth-time product, for quantitative phase imaging of unstained live
samples in vitro. Microscope lenses can have either large field of view (FOV)
or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new
computational imaging technique that circumvents this limit by fusing
information from multiple images taken with different illumination angles. The
result is a gigapixel-scale image having both wide FOV and high resolution,
i.e. large space-bandwidth product (SBP). FPM has enormous potential for
revolutionizing microscopy and has already found application in digital
pathology. However, it suffers from long acquisition times (on the order of
minutes), limiting throughput. Faster capture times would not only improve
imaging speed, but also allow studies of live samples, where motion artifacts
degrade results. In contrast to fixed (e.g. pathology) slides, live samples are
continuously evolving at various spatial and temporal scales. Here, we present
a new source coding scheme, along with real-time hardware control, to achieve
0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an
improved algorithm and new initialization scheme, which allow robust phase
reconstruction over long time-lapse experiments. We present the first FPM
results for both growing and confluent in vitro cell cultures, capturing videos
of subcellular dynamical phenomena in popular cell lines undergoing division
and migration. Our method opens up FPM to applications with live samples, for
observing rare events in both space and time
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