117 research outputs found
Correcting motion induced fluorescence artifacts in two-channel neural imaging
Imaging neural activity in a behaving animal presents unique challenges in
part because motion from an animal's movement creates artifacts in fluorescence
intensity time-series that are difficult to distinguish from neural signals of
interest. One approach to mitigating these artifacts is to image two channels;
one that captures an activity-dependent fluorophore, such as GCaMP, and another
that captures an activity-independent fluorophore such as RFP. Because the
activity-independent channel contains the same motion artifacts as the
activity-dependent channel, but no neural signals, the two together can be used
to remove the artifacts. Existing approaches for this correction, such as
taking the ratio of the two channels, do not account for channel independent
noise in the measured fluorescence. Moreover, no systematic comparison has been
made of existing approaches that use two-channel signals. Here, we present
Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove
artifacts by specifying a generative model of the fluorescence of the two
channels as a function of motion artifact, neural activity, and noise. We
further present a novel method for evaluating ground-truth performance of
motion correction algorithms by comparing the decodability of behavior from two
types of neural recordings; a recording that had both an activity-dependent
fluorophore (GCaMP and RFP) and a recording where both fluorophores were
activity-independent (GFP and RFP). A successful motion-correction method
should decode behavior from the first type of recording, but not the second. We
use this metric to systematically compare five methods for removing motion
artifacts from fluorescent time traces. We decode locomotion from a GCaMP
expressing animal 15x more accurately on average than from control when using
TMAC inferred activity and outperform all other methods of motion correction
tested.Comment: 11 pages, 3 figure
Improved synapse detection for mGRASP-assisted brain connectivity mapping
Motivation: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy
Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis
Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem
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
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