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Unmatched ROIs in predicted segmentation and provided FastSurfer_ColorLUT.tsv #514
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That is expected and correct behavior. Moreover, you will see that the indices of the segmentation classes are also very different between the FastSurfer_ColorLUT.tsv and the output image. |
If you are just using FastSurfer via FastSurfer/FastSurferCNN/run_prediction.py Lines 398 to 413 in 16335a9
I am not really sure what you are really comparing right now; FreeSurfer segmentation with FastSurfer's It seems to me you are manually calling just Inference.run and skipping the post processing steps in RunModelOnData.get_prediction. |
Also, I am not sure about which metrics you are using in your plots? Is this correlation of volumes? Those numbers look awfully low... And with comparisons between FreeSurfer and FastSurfer it will always be difficult to say what is better if you compare them with respect to each other. Furthermore, cortical regions evaluated on the volume can always be very misleading. All that said, we run per-release continuous validation of FastSurfer and make sure the metrics presented in both the FastSurfer and the FastSurferVINN paper are still satisfied. Specifically, this means Test-Retest metrics such as https://www.sciencedirect.com/science/article/pii/S1053811920304985#fig9 and Dice evaluations. |
Yes, if you want to compare volumes for the structures, use the values in aseg.stats or aparc+aseg.stats after a full run of FastSurfer (including the surface module), as we correct some volumes with the surfaces similar to FreeSurfer. |
Yes. Exactly. The results I showed are after running the |
Even |
Well. I expected two things : the segmented image and the volume stats corresponding to the segmentation. In the earlier post, all the results were collected after I run the |
That all sounds correct, the only thing that does not match up is that the statistics you presented up top make no sense as an output to To achieve this, we recommend running fastsurfer with the
Makes no sense to me, I have never seen this issue and I cannot reproduce this. |
The example I was using is from the Tutorial Notebook (https://github.com/Deep-MI/FastSurfer/blob/dev/Tutorial/Complete_FastSurfer_Tutorial.ipynb).
All the codes are run on a MacBookPro with M1 Max chip, current OS version is 14.4.1 (macOS Sonoma). The FasterSurfer version is the latest Stable branch By checking the segmented images, I have noticed that six cortex regions are missing from the |
First off, thank you for the detailed report. |
Have you tried running FastSurfer (i) with run_fastsurfer --seg only and ii) the full pipeline ? I would not expect missing regions in either of those two options. I think that the problem comes from something you are doing differently from how FastSurfer does it. |
Yep. I tried the |
You are using the latest release version, right? And a native install or inside Docker? |
I used the latest release version and the native install. Besides that, I also tried to run the same python scripts in a GCP VM and the results are consistent with that on my MacPro. I will also re-setup and try the |
FYI. Thanks for your patience and response. I reset-up the native install and rerun the
And the resulted
I covered my Username due to private concern. But that wouldn't affect the result. |
I'm unable to reproduce the issue. In the meantime, could you share the deep-seg.log file here? |
deep-seg.log |
Yes, Sure. |
@zainthecoder Note that this seems to be a native install on a modern Mac Book (with M chip). |
@gmeng92 I just merged #530 into the dev branch. This will make it easier to install and run FastSurfer on your M1 Mac. The only caveat is that the dev version of FastSurfer is not tested. But you could give it a try. Also in order to replicate your results we would need your image and run it on a Mac and Linux. We do not expect regions to be missing and it indicates either a problem in the way you run it or a problem with the quality of the image (or simply out-of-distribution). |
You could also try to run your setup on one of the tutorial images:
If it gives the same missing regions, it is your setup that has issues. If not, it is your image. |
Closing this for now due to inactivity and could not replicate. |
Question/Support Request
I run the FastSurferCNN (--seg-only) to get the DKT atlas segementaion given t1W MR images.
By using my local images, I ended up get 88 unique ROI labels(including the background = 0), which is inconsistent with the ROI list as /coifg/FastSurfer_ColorLUT.tsv. Then I followed your tutorial to run the --seg-only code using the image you provided as in (https://github.com/Deep-MI/FastSurfer/blob/dev/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb), it turns out to 88 unique ROI labels again.
So the provided labels (in the tsp) seems unmatched with the predicted labels (from segmented images). Beisides, I noticed that in the tutorial notebook, I find the cell under "Click3" saying that 95 predicted labels are expected. And all those mismatches happen to be on the right hemisphere cortex. Can you help me elaborate those unmatched labels? Thanks
Screenshots / Log files
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