Comments (1)
Unfortunately there is no easy answer to this.
Peaks-tracking is doing tracking on the predicted peaks (Tract Orientation Maps). Moreover it is using a custom tracking implementation which is similar to a deterministic tracking but at each step adds a small random jitter to make it more sensitive. In the end the streamlines are smoothed a little bit to make the streamlines less scrawly.
FOD-tracking is doing tracking on the FODs. It is using the mrtrix iFOD2 tracking.
So you have 2 different tracking algorithms and 2 different orientation sources (predicted peaks and original FODs). Now you have to decide which one is better for your needs.
There is no straight forward way to get the best out of both.
from tractseg.
Related Issues (20)
- loss become minus when train my own TOM model
- Definition of AF and SLF-III HOT 2
- Apply TractSeg to the study specific population template using the peaks of the spherical harmonic function at each voxel as input HOT 9
- error message when tracking HOT 10
- About trk_2_binary.py HOT 4
- No such file or no access "template/TOM/bundle_segmentations/AF_left.nii.gz HOT 1
- Very minor setup.py problem HOT 1
- About bundle_masks.nii.gz
- Plot3D error HOT 1
- CUDA error with M1 chip HOT 2
- Failed to install TractSeg in Ubuntu18 HOT 1
- [Explanation] Training slice-wise and predicting subject-wise? HOT 2
- EOFError: Ran out of input HOT 6
- download_all_pretrained_weights does not flag when it cannot download to folder HOT 1
- Tractometry results plots with negative average metric values HOT 3
- Training Process Question HOT 2
- Boundles output empty or not well segmented HOT 2
- Tracking issue : TypeError: 'NoneType' object is not subscriptable HOT 2
- Loading weights error HOT 2
- Import Error HOT 6
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from tractseg.