2,220 research outputs found
Modeling Brain Circuitry over a Wide Range of Scales
If we are ever to unravel the mysteries of brain function at its most
fundamental level, we will need a precise understanding of how its component
neurons connect to each other. Electron Microscopes (EM) can now provide the
nanometer resolution that is needed to image synapses, and therefore
connections, while Light Microscopes (LM) see at the micrometer resolution
required to model the 3D structure of the dendritic network. Since both the
topology and the connection strength are integral parts of the brain's wiring
diagram, being able to combine these two modalities is critically important.
In fact, these microscopes now routinely produce high-resolution imagery in
such large quantities that the bottleneck becomes automated processing and
interpretation, which is needed for such data to be exploited to its full
potential. In this paper, we briefly review the Computer Vision techniques we
have developed at EPFL to address this need. They include delineating dendritic
arbors from LM imagery, segmenting organelles from EM, and combining the two
into a consistent representation
Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images
Automated sample preparation and electron microscopy enables acquisition of
very large image data sets. These technical advances are of special importance
to the field of neuroanatomy, as 3D reconstructions of neuronal processes at
the nm scale can provide new insight into the fine grained structure of the
brain. Segmentation of large-scale electron microscopy data is the main
bottleneck in the analysis of these data sets. In this paper we present a
pipeline that provides state-of-the art reconstruction performance while
scaling to data sets in the GB-TB range. First, we train a random forest
classifier on interactive sparse user annotations. The classifier output is
combined with an anisotropic smoothing prior in a Conditional Random Field
framework to generate multiple segmentation hypotheses per image. These
segmentations are then combined into geometrically consistent 3D objects by
segmentation fusion. We provide qualitative and quantitative evaluation of the
automatic segmentation and demonstrate large-scale 3D reconstructions of
neuronal processes from a volume of brain
tissue over a cube of in each dimension corresponding to
1000 consecutive image sections. We also introduce Mojo, a proofreading tool
including semi-automated correction of merge errors based on sparse user
scribbles
Pathway-Based Genomics Prediction using Generalized Elastic Net.
We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Supervised Parameter Estimation of Neuron Populations from Multiple Firing Events
The firing dynamics of biological neurons in mathematical models is often
determined by the model's parameters, representing the neurons' underlying
properties. The parameter estimation problem seeks to recover those parameters
of a single neuron or a neuron population from their responses to external
stimuli and interactions between themselves. Most common methods for tackling
this problem in the literature use some mechanistic models in conjunction with
either a simulation-based or solution-based optimization scheme. In this paper,
we study an automatic approach of learning the parameters of neuron populations
from a training set consisting of pairs of spiking series and parameter labels
via supervised learning. Unlike previous work, this automatic learning does not
require additional simulations at inference time nor expert knowledge in
deriving an analytical solution or in constructing some approximate models. We
simulate many neuronal populations with different parameter settings using a
stochastic neuron model. Using that data, we train a variety of supervised
machine learning models, including convolutional and deep neural networks,
random forest, and support vector regression. We then compare their performance
against classical approaches including a genetic search, Bayesian sequential
estimation, and a random walk approximate model. The supervised models almost
always outperform the classical methods in parameter estimation and spike
reconstruction errors, and computation expense. Convolutional neural network,
in particular, is the best among all models across all metrics. The supervised
models can also generalize to out-of-distribution data to a certain extent.Comment: 31 page
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