Team dramatically reduces image analysis times using deep learning,
other approaches

Date:
June 29, 2020
Source:
Marine Biological Laboratory
Summary:
Scientists have devised deep-learning and other approaches that
dramatically reduce image-analysis times by orders of magnitude --
in some cases, matching the speed of image data acquisition itself.



FULL STORY ==========================================================================
A picture is worth a thousand words -but only when it's clear what
it depicts.

And therein lies the rub in making images or videos of microscopic
life. While modern microscopes can generate huge amounts of image data
from living tissues or cells within a few seconds, extracting meaningful biological information from that data can take hours or even weeks of
laborious analysis.


==========================================================================
To loosen this major bottleneck, a team led by MBL Fellow Hari Shroff
has devised deep-learning and other computational approaches that
dramatically reduce image-analysis time by orders of magnitude -- in
some cases, matching the speed of data acquisition itself. They report
their results this week in Nature Biotechnology.

"It's like drinking from a firehose without being able to digest what
you're drinking," says Shroff of the common problem of having too
much imaging data and not enough post-processing power. The team's improvements, which stem from an ongoing collaboration at the Marine
Biological Laboratory (MBL), speed up image analysis in three major ways.

First, imaging data off the microscope is typically corrupted by
blurring. To lessen the blur, an iterative "deconvolution" process is
used. The computer goes back and forth between the blurred image and an estimate of the actual object, until it reaches convergence on a best
estimate of the real thing.

By tinkering with the classic algorithm for deconvolution, Shroff and co- authors accelerated deconvolution by more than 10-fold. Their improved algorithm is widely applicable "to almost any fluorescence microscope,"
Shroff says. "It's a strict win, we think. We've released the code and
other groups are already using it." Next, they addressed the problem
of 3D registration: aligning and fusing multiple images of an object
taken from different angles. "It turns out that it takes much longer to register large datasets, like for light-sheet microscopy, than it does to deconvolve them," Shroff says. They found several ways to accelerate 3D registration, including moving it to the computer's graphics processing
unit (GPU). This gave them a 10- to more than 100-fold improvement in processing speed over using the computer's central processing unit (CPU).

"Our improvements in registration and deconvolution mean that for datasets
that fit onto a graphics card, image analysis can in principle keep
up with the speed of acquisition," Shroff says. "For bigger datasets,
we found a way to efficiently carve them up into chunks, pass each chunk
to the GPU, do the registration and deconvolution, and then stitch those
pieces back together.

That's very important if you want to image large pieces of tissue, for
example, from a marine animal, or if you are clearing an organ to make
it transparent to put on the microscope. Some forms of large microscopy
are really enabled and sped up by these two advances." Lastly, the team
used deep learning to accelerate "complex deconvolution" - - intractable datasets in which the blur varies significantly in different parts
of the image. They trained the computer to recognize the relationship
between badly blurred data (the input) and a cleaned, deconvolved image
(the output). Then they gave it blurred data it hadn't seen before. "It
worked really well; the trained neural network could produce deconvolved results really fast," Shroff says. "That's where we got thousands-fold improvements in deconvolution speed." While the deep learning algorithms worked surprisingly well, "it's with the caveat that they are brittle,"
Shroff says. "Meaning, once you've trained the neural network to recognize
a type of image, say a cell with mitochondria, it will deconvolve those
images very well. But if you give it an image that is a bit different,
say the cell's plasma membrane, it produces artifacts. It's easy to
fool the neural network." An active area of research is creating neural networks that work in a more generalized way.

"Deep learning augments what is possible," Shroff says. "It's a good
tool for analyzing datasets that would be difficult any other way."

========================================================================== Story Source: Materials provided by Marine_Biological_Laboratory. Original written by Diana Kenney. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Min Guo, Yue Li, Yijun Su, Talley Lambert, Damian Dalle Nogare,
Mark W.

Moyle, Leighton H. Duncan, Richard Ikegami, Anthony Santella, Ivan
Rey- Suarez, Daniel Green, Anastasia Beiriger, Jiji Chen, Harshad
Vishwasrao, Sundar Ganesan, Victoria Prince, Jennifer C. Waters,
Christina M.

Annunziata, Markus Hafner, William A. Mohler, Ajay B. Chitnis,
Arpita Upadhyaya, Ted B. Usdin, Zhirong Bao, Daniel Colo'n-Ramos,
Patrick La Riviere, Huafeng Liu, Yicong Wu, Hari Shroff. Rapid image
deconvolution and multiview fusion for optical microscopy. Nature
Biotechnology, 2020; DOI: 10.1038/s41587-020-0560-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200629150539.htm

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