simplified use of manual segmetation #325
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added 2 commits
December 12, 2024 16:26
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check should pass with #326 |
* fix for using T1T2w model run_inference was only accepting a single input image, this makes it accept a list of images too, and makes it so --force-nnunet-model T1T2w grabs both T1w and T2w as inputs. * hipp curvature was being used always, instead of wildcard this didn't affect any workflow computations, just the subsequent dscalar.nii and spec file that would have the wrong curvature map * fix logical error * Bump version to 1.5.2-pre.1 * Bump version to 1.5.2-pre.2 * keep pipeline_description changes --------- Co-authored-by: Ali Khan <akhanf@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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double check common.smk |
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I've had many users get tripped up on this (myself included!), often when using ex-vivo data. Here, I provide a single
--modalityoption for manual segmentations. I add the requirement that the data be roughly aligned with one of the input templates (e.g. CITI168). I believe this is a simple requirement given the complexity anyone has to go through in doing a manual segmentation anyway. Also ex-vivo cases tend to be in arbitrary spaces anyway, making it very hard to work with until they are reoriented.I also restore the lut, which I will ad to the Documentation guiding manual segmentations (TODO!)
The template is still used to go to space-corobl (with cropping), so users don't need to crop around the hippocampus. So, effectively, I'm renameing
cropsegtomanualseg, adding the template-to-corobl transform, and remove thesegT1wandsegT2wworkflows.