Comments (7)
Can you post the precise set of commands used to produce these results?
from antsr.
After downloading as above, I did:
plot(mymni[[1]], mymni[[2]], alpha=.8)
I also note that if I threshold the MNI at values 1 and higher (which surely includes some CSF), and plot the AAL mask on that, it still doesn't match up perfectly:
> mniMask2<-thresholdImage(mymni[[1]],1,Inf)
> plot(mniMask2, mymni[[2]], alpha=.8)
which results in this image:
antsMNIextremeMask_with_AAL_overlay.pdf
If I do a 3-category kmeans segmentation, combine presumed gray and white matter portions of this, and then plot the AAL on top, it shows even less of a match to the surface:
> mni_kmeans3_seg<-kmeansSegmentation(mymni[[1]], 3)
> mni_kmeans3_GW<-thresholdImage(mni_kmeans3_seg$segmentation,2,3)
> plot(mni_kmeans3_GW, mymni[[2]], alpha=.8)
Which results in this:
antsMNIkmeans3Mask_with_AAL_overlay.pdf
I realize things won't be exact, but I just want to be sure I'm not misunderstanding something here.
My thought is the following: The original segmentation of the MNI was a best-guess to begin with, of course. Since my ultimate goal is to compare populations with respect to size of different AAL areas, then as long as I register the MNI atlas to each individual within both populations the same way, the differences between these populations in average AAL area sizes will likely be reasonable, even if the registration to any given individual is imperfect, and even if the AAL itself doesn't exactly match the MNI atlas used. This assumes either random errors or the same biases in the same ways in both populations. Given that the study I'm comparing my present work to essentially ignored these issues, this seems reasonable to me - but I'd be interested in any thoughts.
-Tom
from antsr.
yes - AAL labels are very crude, from the start. undergrads paid to roughly segment structures - done something like 20 years ago.
from antsr.
OK. Any thoughts about why the AAL doesn't match the MNI particularly closely (the areas were drawn on a version of the MNI, so it seems odd to me they wouldn't match more closely)? Also: are there other segmentation akin to the AAL that anyone might recommend in place of them?
-Tom
from antsr.
they are intended to be masked by tissue after the fact
from antsr.
That makes sense. Is there a published reference by any chance that makes this clear, that I can include in my manuscript?
-Tom
from antsr.
My go-to for all things MNI is the LEAD-DBS page
from antsr.
Related Issues (20)
- Installation fails on Ubuntu 22.04LTS with R4.2.0 HOT 3
- ERROR: Parsing error: The keyword 'const' is reserved
- Errors in buliding ANTsR vigniettes HOT 1
- installation problem antsLibs() Makevars:6: *** missing separator. Stop. HOT 5
- Error
- antsMotionCalculation Error HOT 1
- colorbar key for antsrSurf HOT 6
- Installation fail with devtools HOT 5
- details about mnib HOT 1
- Failing on ITK when running LINDA HOT 6
- Proper similarity metric for Affine registration HOT 3
- MVPA using ANTsR?
- ITK bug in createJacobianDeterminantImage after upgrade HOT 14
- aslPerfusion HOT 11
- Installation error on Mac OS 12.6.3 with devtools HOT 11
- R version issues HOT 3
- ITK_GLOBAL_NUMBER_OF_THREADS is ignored HOT 2
- How to pass command line arguments to antsRegistration? HOT 4
- Guidance on Extracting Numeric Values of Gaussian Curvature from Image Class HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from antsr.