2,028 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)
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Detecting, segmenting and tracking bio-medical objects
Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking.
In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases --Abstract, page iii
Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning
Brillouin imaging relies on the reliable extraction of subtle spectral
information from hyperspectral datasets. To date, the mainstream practice has
been using line fitting of spectral features to retrieve the average peak shift
and linewidth parameters. Good results, however, depend heavily on sufficient
SNR and may not be applicable in complex samples that consist of spectral
mixtures. In this work, we thus propose the use of various multivariate
algorithms that can be used to perform supervised or unsupervised analysis of
the hyperspectral data, with which we explore advanced image analysis
applications, namely unmixing, classification and segmentation in a phantom and
live cells. The resulting images are shown to provide more contrast and detail,
and obtained on a timescale faster than fitting. The estimated spectral
parameters are consistent with those calculated from pure fitting
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