Papayar: Interactively Viewing Images in R

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Del curso dictado por Johns Hopkins University

Introduction to Neurohacking In R

133 calificaciones

Johns Hopkins University

Introduction to Neurohacking In R

133 calificaciones

Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).

De la lección

Extended Image Processing

In this section, we will discuss the different types of registration and how one would go through processing a multi-sequence MRI scan, as well as wrapper functions that make the process much easier. We also cover interactive exploration of brain image data and tissue-level (white/gray matter and cerebrospinal fluid (CSF)) segmentation from a T1-weighted image.

Papayar: Interactively Viewing Images in R22:52

Conoce a los instructores

Dr. Elizabeth Sweeney
Rice Academy Postdoctoral Fellow
Biostatistics
Ciprian M. Crainiceanu
Professor
Biostatstics
John Muschelli III
Assistant Scientist
Biostatistics

So this will be somewhat of a bonus lecture talking about

interactive image viewers in R.

So what I'm going to do is show you how to manipulate

images interactively using an R package.

And I'm also going to show you some other image viewers that are cross platform,

that allow you to look at images interactively, so you can explore your

data a little bit more fluidly than just plotting certain images statically.

So I'll show you what I mean in a minute.

So I'm going to load up the FSLR package, because I want to read in an image.

Right, again, you could also do this with oro.nifti.

But right now, I'm in the kirby21\visit_1, patient 113 folder, right,

so this is one patient and I'm going to read in the MPRAGE, the T1 weighted image.

So as you see, image is this NIfTI object,

if I say the dim, so the dimensions

are 170 x 256 x 256, right.

And we could use the orthographic, or the ortho2 function, to actually plot it.

So okay, so we see here we can't really click anything, but

it shows you one slice of the data, in some respects.

So there are interactive viewers out there that allow you to click around.

So one of the ones we talked about, so there's fslview,

so if we actually put the image in there,

the NIfTI object, or the file name,

that will allow FSL's native viewer to be run and I'll load it up.

So again, all right, we can look through this, we can zoom in.

Right, we can look at different areas there.

So, fslview is one option to do interactive

plotting, right, but as you see, it popped up a different window and

a different window that is kind of based on if you have FSL view installed.

And what we wanted to do, even though in our course FSL is assumed to be

installed on your virtual machine, we want to show you something that's native to R.

All right, so you see some warnings and stuff like that, and

that's due to FSL, actually FSLR.

So, again, FSL gives you one way to look at a viewer interactively.

There are some different viewers out there that some people think are better

or worse.

And one of them out there is called Mango.

So if you just Google, Mango viewer.

So you look at the Research Imaging Institute and Mango.

So it has a lot of information here about what it supports and

all this kind of stuff.

But Mango's, again,

a third party piece of software, a piece of software that's not included in R.

And I'm going to bring it up right now.

That allows you to look at slices of an image interactively.

So let's open an image.

So again, we're going to take the MPRAGE, it's going to load it up.

And so we should be able to click through different slices, top,

towards the bottom of the brain looking at different structures.

You can change the windowing in some respects to highlight certain aspects of

the brain, darken areas to look at more of the white matter or

something in some respects.

You can overlay things so you can add regions of interest,

you can get statistics, you can get statistics over regions of interests.

For example, you can do a histogram.

Again, I wouldn't necessarily suggest to do a histogram here,

although it's a good utility, we've really striven to be working with data in R.

So you can easily, for example, go back to your log and

say hist(c(img)) and we can see that histogram.

You can also say if the image is greater than 0.

Let's make the breaks, a lot of breaks.

So we can also do things like,

Let's find the 10th quantile.

Let's maybe say and not write for the (img[img > 0].

And again, so these are things we can do interactively to try to

find certain things so we can actually show the histogram for.

We are still seeing a huge spike at 0.

We'll actually start towards the lower end.

So I'm going to make this like 10,000 or something like that to try.

So we can see certain parts of the distribution.

And again, this is really good because it doesn't necessarily,

it shows you more bins, right, we chose to do 200, it did a little bit more.

But if we did the min would be 0 here.

It would show you something similar to that.

But okay, so Mango is a good viewer especially to view multiple images.

But again, this is a third party piece of software but I think it's pretty good.

I know a lot of collaborators and I tend to use this for certain things.

I also like FSLView.

The other one we're going to talk about is called ITK-SNAP, and

this is a piece of software coming out of Penn.

So this is another good image viewer if you want to do segmentations or tracings,

it's also a very good piece of software for that, usually it's pretty fast.

So if you download it you'll get the.

If you want to allow it to be open, so yeah, let's just browse.

I'm going to go back to the data we've been talking about.

All right so, MOOC\data\kirby21\visit_1,

patient 113, we're going to go to MPRAGE.

Correctly identifies that it is NIfTI.

As I finish, and again, we can view different slices.

This is a three dimensional rendering that you can do.

And there's a lot of different functionalities and it's hard to

necessarily go through every single one of them in each one of these packages.

So they also have really good documentation.

So you can zoom in like you did in FSL.

All right, we'll do twice, four times, so

we can zoom in, zoom to fit, center on cursor.

So we can change around certain things but

this allows you to look at different slices of the brain

kind of interactively versus just getting one snapshot view of like Ortho2.

Now that being said, for reproducability we do suggest that if you're going to do

publication figures or anything like that, that I would do that in R using Ortho2 or

the image function to actually plot the data so that I can reproduce it later.

Right, so we can again adjust the contrast.

This is kind of similar to the transfer function that Elizabeth should've touched

on previously.

So anyway, these are good pieces of software that are not specific to R,

they have nothing to do with R, that allow you to interactively view your images.

So, that being said, this class is very, very heavy on R, so

I'm going to talk about a package called Papaya R.

So if you go the Mango web page it says there's Mango, which is for

all three main operating systems.

And then they've implemented this image viewer in a system called Papaya, right,

so it's a little cute because they have Mango and now it's Papaya.

But it's written completely in JavaScript so you can view your images on a webpage.

So if you go the Papaya webpage,

it shows you kind of how to look at certain things like that.

And so, I think it'll, yeah, and so if you actually click here to Run,

it'll show you what comes up.

So we can add an image, all right, again let's go to,

Visit 113, Kirby 21, all that fun stuff.

So again, this is a nice lightweight browser.

So, there aren't as many options, right?

So that is actually somewhat of a good thing, if you're starting out,

to have the options be relatively limited, right?

So you can see the orientation,

you can see a ruler to actually get some measurements on these things.

And again, I would use these for interactive viewing.

I wouldn't necessarily use them for figure generation,

unless you made a really good figure, for example, and you

couldn't figure out how to do it in R, but it was super, super hard to do it here.

So, there's other fun overlays.

Again, we only looked at the histogram.

This histogram is a little bit wide or

long I guess, in some respects.

So you can do rotations, if you want to rotate the head,

like it's looking down for some reason now.

I don't know why, necessarily, people do that, but again,

you can view different things, and you can view regions of interest.

So, swap the views to look at the different parts, so now the sagittal

view's in plane, the axial is over here, the coronal is over here.

Again, we go to just the center and this is kind of the center of the brain.

So this is papaya.

And the nice thing about it being written in JavaScript

is that we can wrap an R package around it, which I did,

to allow you to pass in objects from R and have it interactively view.

So, sorry, by the time this

package error or this lecture errors, we should be able to

install papayar the same way you normally would install .packages.

So currently, and if you also want to download the development version of

papayar you can use devtools.

So, you have to have the devtools package installed, if not,

you can say install.packages.devtools.

But they should install it, but I already have it installed so

I'm not going to go through that.

But again, remember, we have image, which is a nifty object, right?

And let's say, something over 500,000.

So let's do an overlay real quick of ortho2(img.

img > 5e5).

Right? So now,

we're just going to display the image again.

But we're also going to put in a region of interest on top of it that says highlight

the voxels, that are over 500,000, so that's what we're looking at here.

These are the voxels in red that have values over 500,000.

So, what I'm going to do is call that mask, okay?

Highlight this one for a second.

So now, oops, I accidentally just resourced everything, but

we're going to let that run and I'm going to talk a little bit.

So what we're going to do is pass this into papaya, all right?

So that's the function that you pass in images.

So, you can either pass in file names or

you can pass in nifty objects, but it has to be in a list if it's nifty objects.

So we're going to put in the image and the mask.

So once we do this, we're going to wait a second and

it's going to write these out to a specific folder, and

then we're going to be able to view these interactively within the R Studio window.

All right, so that's what's really nice, you're all within R, right?

It's a little small, but we can make that a lot bigger.

And I'll show you how to do this at the command line if you want something to pop

up that is a little bit better in your browser.

So again, this is a mask.

It just goes from 0 to 1.

So we can actually change it so

the colors are more transparent.

So we should be able to change it, and say positives.

And then, I think we'd say 1 to 1.

Nope, so I'll undo.

0 to 0.5.

So, right, we can hide the overlay so we can toggle on and off.

But for example, if this is a region of interest or a segmentation,

you'll be able to view the image and interactively go

through the slices without having to plot ortho2 over, and over, and over again.

So, I think this is a nice way to interactively view your data.

It's all within R if you're using RStudio, it'll have a viewer set up here.

So right now I'm going to show you, what if you were using,

I'm going to make this a little bit bigger, the R app.

And I'm going to want to do something very similar over here.

I'm going to run these three lines.

And again, we're on the same code,

but it's not going to show up in an R Studio viewer.

It's going to show up in a web browser.

So, whatever your web browser may be, in this case, my default is Chrome,

it'll actually show you the same things.

Let me change the color to the blue, no.

Okay, so, now yellow is the mask.

So, this starts a server on your computer,

it loads up these images and allows you to interactively view it.

When you close it, you can go right back to your R session.

Let me just kill the command.

And then you can continue going on and say like,

I saw ROI is bad in this slice, and you can change certain things.

But that is, in some respects, the long and

short of running papayar in interactive viewing.

Now, there is another package called itksnapr.

Which I'll show you, for example, how that you can install it.

But give me one second, I actually have to stop that command

from running because it runs interactively.

So, it should download this package

right here, it'll load in a second.

So, it might take a second to actually download.

And again, you have to make sure you're on the Internet for all this.

So it's going to download, it's going to install.

Just going to clear out that.

And, clear some of these Plots.

So, it should be installing on your system, and

then we would load it up the same way we load up anything else.

So, now there's a function called ITK-SNAP.

And so right now, grayscale, so

I'm going to actually just put in the image that we were interested in.

And so you just say itksnap.

So now, this, more or less,

will just pre-package an ITK-SNAP

executable and install it somewhere on your system,

wherever that R-package is installed, and allow you to run things interactively.

So this is not really staying within R, right?

Papayar will allow you to stay within R in the sense it'll start a web browser, and

you can view it that way and stay in R Studio.

This is just allowing, ITK-SNAP, allowing you to pass in, for

example, NIfTI objects to a R function that'll load a viewer.

So it is very similar to FSLView in that respect.

But this is still a third party viewer, but

it's still a good way to potentially view things interactively.

To get an idea of your data interactively to see what's really going on.

So I recommend Papayar because I think it's nice to have certain things

within the browser window that you're actually doing and also, for example,

in your web browser.

But overall, this is a way you can interactively explore your data without

having to do image(img, c = 1, 2, 3, 4) or look at some lightbox view, right?

So I'll show you what I mean by that.

because if you just say image on a NIfTI object it'll, by default,

plot every single slice of the brain because default is

not to do plot.type = single, which we actually showed here.

While that's loading you can say,

And here I'm loading oro.nifti instead of fslr, but it doesn't really matter.

because fslr loads oro.nifti by default.

So if I say image.nifti,

It says it produces a Lightbox view of images for nifti, anlz or afni objects.

And you see plot.type by default, because it's the first,

is multiple, which means it'll plot out every single slice of an image.

This is taking a little bit of time actually for

R Studio, and that can happen sometimes when you have some

images that you're going to plot that can be somewhat big, [INAUDIBLE] image(img).

All right, you see it goes through every single slice and it plots that.

And they're almost like thumbnail size, so that's not really great.

Right, let me kill that off real quick.

Is a z equals 90,

180.

Whoops, so we can say this, but it's not going to

do what you necessarily think it's going to do.

All right, still thinks it wants you to do all this.

So, you have to say plot.type = single.

So that's a little bit unintuitive for the image.nifti, right,

but if I want to do 90, let's say 30, 90, 180, and 200 on these plots.

It will show you those specific slices.

And you can also put, oops, you want to do image.nifti for help.

Right, and so this is if you have four dimensions,

you want to also specify w, right?

So that you accurately can specify which time point that you want to look at,

but again if you wanted to put overlays, right?

because I've been showing you how to, Do x and

y, or show the image with some threshold of value and

I'll show you how to do that in a second.

So let's, instead of this, let's say mask = img is greater than 5e5, right?

So like we were doing over here, so again, this is

a little bit of a lagginess in R Studio in some respects,

that's why I'm kind of working in the terminal right now.

Img, mask, let's say

all this same options.

Something went a little weird here.

Img, mask, z =

So it doesn't actually seem like it did what we wanted.

We can say mask(mask = 0) = NA.

So we actually have to take out values that are zero,

otherwise they are going to get mapped to that.

And this is, for example, showing what it would like if you overlaid them.

We can also change col.y="red".

So now we're saying these are the values over 500,000 on the slices of 30,

90, 180, 200.

But again, papayar will allow to do this kind of interactively,

you have play with some of the controls, kind of get a handle on it and

we're not going to go into all those controls here.

But it's good at least for the very basics of putting an image and

an overlay and you use this papaya command.

There is the ITK-SNAP R, if you've ever used that and

you're more comfortable with that.

FSLView also will allow you to do an interactive viewer that

has a little bit more tools, has the ability to do histograms.

If you want to install Mango on your own that is also an option.

But the nice thing about Papaya, or Papayar,

is it allows you to manipulate objects in R like you normally would, and

you just pass in a list of those NIfTI objects.

Just like we've been showing you with some of the other commands and

that's how you can do interactive plotting in your day to day in Papayar, and

if you're using R Studio, will allow you to look at things through the viewer pane,

which I think is very nice.

And it'll actually keep that up for some time as long as you don't cancel this out.

So we can actually close that, and I'll actually close that connection.

But overall, Papaya is great because it's written in pure JavaScript so

that we don't have to depend on a lot of things for R to be able to use it.

Some of the functions, I think some of the functions in oro.nifti

are really good that have been expanded a bit in fslr like ortho2.

They're really good, I think, for customizing figures and

trying to get figures in the way you want them, but they're generally only 2D, but

for interactive viewing.

So if we're scrolling through images, if we're scrolling through slices,

I recommend using papayar, itksnap, or itksnapr, mango, or maybe fslview.

So I think these are some of the options that you have to explore your data

a little bit to find maybe somewhere where some artifacts are,

somewhere some oddities are in your data.

But this is a step towards interactive viewing that is very helpful in analysis.

All right, thank you.

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