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brainlife / app-tractographyQualityCheck

Compute many statistics from your input tractogram and any (optionally input) associated classification structure. These statistics can be used to facilitate quality assurance on your tractography and segmentation, or as part of subject/group level quantative analysis for a research project. See the output section of README.MD for more details.
MIT License
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Porting to python / providing definitions of useful measures #9

Open bacaron opened 3 years ago

bacaron commented 3 years ago

Hey Dan,

Per slack discussion, would you be able to get me the definitions for the measures of interest from this app? I will then integrate them into the python-based dipy version of this app (https://brainlife.io/app/5f794bde268f765c3929bda2).

Thanks, Brad

DanNBullock commented 2 years ago

wma_pytools has some of this functionality here. I need to spend some time documenting and organizing wma_pytools in general. May do that over the holidays.

The linked code doesn't have all of the computations found in the matlab output though. Also, some of these likely need to be changed (e.g. the midpoint computations turn out to not be the ideal quantification for the "neck" of a tract). But I was planning on porting this over to pandas soon enough. Also switching over to using matplotlib/seaborn for the output.

The definitions are found in the (arguably overdone) readme for the app.

bacaron commented 2 years ago

@DanNBullock I'm responding to this again due to conversations recently brought up regarding reference datasets and defining the common columns of the tractmeasures (macro-structural statistics) datatype.

I have already started an app to port this to python: https://brainlife.io/app/5f794bde268f765c3929bda2. This would be a good starting point if you are wanting to port your app to python.

Regardless, I believe you and I need to meet and discuss the common measures and names for this particular subtype of the tractmeasures datatype. This is important for the validators and for the brainlife paper. Due to multiple instances of spelling errors (ex. avgerageStreamlineCount) and some uninformative names (ex. avgefficiencyRat????), we will need to update and potentially rerun apps. Because of this, getting this addressed asap is of decently high importance (to me at least).

Would meeting to discuss this be doable in the next week or so?

DanNBullock commented 2 years ago

Next week, sure. I'm at a neuromodulation conference today and tomorrow.

On Thu, Apr 14, 2022, 1:02 PM Brad Caron @.***> wrote:

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@DanNBullock https://github.com/DanNBullock I'm responding to this again due to conversations recently brought up regarding reference datasets and defining the common columns of the tractmeasures (macro-structural statistics) datatype.

I have already started an app to port this to python: https://brainlife.io/app/5f794bde268f765c3929bda2. This would be a good starting point if you are wanting to port your app to python.

Regardless, I believe you and I need to meet and discuss the common measures and names for this particular subtype of the tractmeasures datatype. This is important for the validators and for the brainlife paper. Due to multiple instances of spelling errors (ex. avgerageStreamlineCount) and some uninformative names (ex. avgefficiencyRat????), we will need to update and potentially rerun apps. Because of this, getting this addressed asap is of decently high importance (to me at least).

Would meeting to discuss this be doable in the next week or so?

— Reply to this email directly, view it on GitHub https://github.com/brainlife/app-tractographyQualityCheck/issues/9#issuecomment-1099475518, or unsubscribe https://github.com/notifications/unsubscribe-auth/ADFXNPL7J7B6RK52ZIA55ADVFBMT5ANCNFSM46DWIXUA . You are receiving this because you were mentioned.Message ID: @.***>

DanNBullock commented 2 years ago

@bacaron in the meantime, can you make like a bullet-pointed list of these and/or other issues? I may be able to preemptively fix and/or otherwise address them prior to the meeting. Could help us make best use of the meeting time.