3D Structure from 2D Microscopy images using Deep Learning
- URL: http://arxiv.org/abs/2110.07608v1
- Date: Thu, 14 Oct 2021 14:55:41 GMT
- Title: 3D Structure from 2D Microscopy images using Deep Learning
- Authors: Benjamin J. Blundell, Christian Sieben, Suliana Manley, Ed Rosten,
QueeLim Ch'ng, and Susan Cox
- Abstract summary: Recent advances in Artificial Intelligence have been applied to retrieving accurate 3D structures from microscopy images.
Herewe present a deep learning solution for reconstructing the protein com-plexes from a number of 2D single molecule localization microscopy images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding the structure of a protein complex is crucial indetermining its
function. However, retrieving accurate 3D structures from microscopy images is
highly challenging, particularly as many imaging modalities are
two-dimensional. Recent advances in Artificial Intelligence have been applied
to this problem, primarily using voxel based approaches to analyse sets of
electron microscopy images. Herewe present a deep learning solution for
reconstructing the protein com-plexes from a number of 2D single molecule
localization microscopy images, with the solution being completely
unconstrained. Our convolutional neural network coupled with a differentiable
renderer predicts pose and derives a single structure. After training, the
network is dis-carded, with the output of this method being a structural model
which fits the data-set. We demonstrate the performance of our system on two
protein complexes: CEP152 (which comprises part of the proximal toroid of the
centriole) and centrioles.
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