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

June 29, 2020

WOODS HOLE, Mass. - 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."
-end-
This paper stems from an ongoing collaboration in the MBL Whitman Center between MBL Fellows Hari Shroff, senior investigator at the National Institute of Biomedical Imaging and Bioengineering; Patrick La Rivière, professor at the University of Chicago; and Daniel Colón-Ramos, professor at Yale Medical School. First author Min Guo is postdoctoral fellow in Shroff's lab and a former teaching assistant for Shroff in the MBL's Optical Microscopy and Imaging in the Biological Sciences course.

The MBL is opening an Image Analysis Laboratory to assist scientists with their analysis of large and complex datasets. More information is here.

The Marine Biological Laboratory (MBL) is dedicated to scientific discovery - exploring fundamental biology, understanding marine biodiversity and the environment, and informing the human condition through research and education. Founded in Woods Hole, Massachusetts in 1888, the MBL is a private, nonprofit institution and an affiliate of the University of Chicago.

Marine Biological Laboratory

Related Learning Articles from Brightsurf:

Learning the language of sugars
We're told not to eat too much sugar, but in reality, all of our cells are covered in sugar molecules called glycans.

When learning on your own is not enough
We make decisions based on not only our own learning experience, but also learning from others.

Learning more about particle collisions with machine learning
A team of Argonne scientists has devised a machine learning algorithm that calculates, with low computational time, how the ATLAS detector in the Large Hadron Collider would respond to the ten times more data expected with a planned upgrade in 2027.

Getting kids moving, and learning
Children are set to move more, improve their skills, and come up with their own creative tennis games with the launch of HomeCourtTennis, a new initiative to assist teachers and coaches with keeping kids active while at home.

How expectations influence learning
During learning, the brain is a prediction engine that continually makes theories about our environment and accurately registers whether an assumption is true or not.

Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.

Learning is optimized when we fail 15% of the time
If you're always scoring 100%, you're probably not learning anything new.

School spending cuts triggered by great recession linked to sizable learning losses for learning losses for students in hardest hit areas
Substantial school spending cuts triggered by the Great Recession were associated with sizable losses in academic achievement for students living in counties most affected by the economic downturn, according to a new study published today in AERA Open, a peer-reviewed journal of the American Educational Research Association.

Lessons in learning
A new Harvard study shows that, though students felt like they learned more from traditional lectures, they actually learned more when taking part in active learning classrooms.

Learning to look
A team led by JGI scientists has overhauled the perception of inovirus diversity.

Read More: Learning News and Learning Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.