Microscopy offers tremendous power
to observe and interrogate biological systems.
In the past, experiments done by microscopy
were typically analyzed by eye, qualitatively.
But now, it's feasible to quantify the phenotypes
that we observe through image analysis.
Images are a powerful readout due to their richness and flexibility.
Microscopy can be used to examine biological systems,
isolate proteins to organelles, to tissues,
all the way up to entire organisms.
Microscopy offers single-cell resolution and multiplexing,
where you detect multiple phenomena using different labels.
And the focus of our video today
is quantifying images through image analysis.
I'm Kevin Eliceiri, a faculty at UW-Madison,
and I lead the open-source ImageJ project.
And I'm Anne Carpenter. I'm an Institute Scientist at the Broad Institute of Harvard and MIT,
and I lead the open-source CellProfiler project.
Both of these are open-source software platforms for image analysis.
But today, we won't be talking about ImageJ or CellProfiler.
There will be supplementary videos on each of these
that you can watch later,
if you'd like to learn more about how they are used.
Instead, this video series is going to focus on the general steps of image processing:
pre-processing, where you prepare your images for analysis;
segmentation, where you identify the biological objects of interest;
tracking, where you're following moving objects over time;
and measurement and phenotype classification,
where the relevant biological properties of interest are quantified.
We'll also cover some best practices for working with bioimage data.
These steps will apply to any kind of image analysis you might do,
whether it's with a commercial tool
or many of the free open-source tools
that you might use.
It also applies whether it's classical image analysis algorithms
or machine learning.
By learning about the basic principles and the common tasks,
you'll be better equipped to work with whatever software you choose.
Speaking of which, how do you choose a specific tool?
There are so many options.
Where do you go for help?
You can search the Scientific Community Image Forum that we started,
at forum.image.sc,
to see if others have worked on your particular biological system or questions.
See our supplemental video about the forum
and hear about this great resource,
and how you can get your questions answered and share the answers to your questions.
You might also check out papers in your field,
or ask colleagues who know your research area,
to see what they used.
And to gather more general knowledge, you can connect to societies such as
NEUBIAS in Europe and BINA in North America.
They offer conferences and training workshops to gain general knowledge,
as well as tutorials for particular software tools.
As well, this video is part of a whole series of microscopy image analysis.
You can check out other iBio... iBiology videos on image analysis,
and also on microscopy hardware and acquisition,
to learn more about the various steps involved in microscopy
and image analysis.
And that's a good time to mention that proper image acquisition
is crucial when you're quantifying,
especially in the case of large experiments,
where you can't examine each image by eye.
With the power of microscopy comes responsibility,
and no amount of fancy image analysis
can overcome some of the kinds of image quality problems.
So, take care to follow proper procedures,
as mentioned in the other videos in this series
and in this blog post linked here,
so that the images you collect will be suited to answer your biological question.
It's always important, as we've mentioned here
and also elsewhere in the video series...
good data in, good data out...
that the key is not to use image analysis to correct for things
that you could have fixed as part of your image acquisition.
It's important to remember to have quality control
to identify issues with your imaging, whether it's blurry because it's out of focus,
maybe the wrong immersion medium is used,
or you have debris in your field.
It's also to know that... it's also important to know the capabilities of your system
so you avoid resolution beyond what you can collect,
both spatially and temporally.
Understand your microscope.
It's also important when you do long time lapse imaging
to minimize bleaching.
You don't want your sample to be overexposed,
to cause problems in later detection
or even affect the health of the organism.
As well, when you look at your microscope,
understand how it's set up, know how to optimize it,
and deal with issues such as intensity variations
due to uneven lighting that may also cause problems for signal-to-noise being too low,
especially for time lapse.
That is where proper controls are critical.
Use controls to set up your microscope that are consistent,
instead of using the real biology to set it up.
So, let's get started quantifying.