Hi Rebecca,
Can you send a screenshot of the grooming parameters you are using? If you are using remeshing (default on), I would try with it off. I have a guess that those warnings and the crash are coming from the remesh library.
You are correct that enabling Procrustes and Procrustes scaling during the optimization should remove global scale from the shape model. I can’t say that it will always remove shape volume from the model as it will depend how closely that matches overall scale. Procrustes scaling in ShapeWorks will iteratively find a scaling (and optionally rotation/translation) to best fit the particles between shapes. But it is uniform in all directions, so a shape can still be longer/shorter than others and have larger/smaller volume.
Thanks, Alan
From:
Beroukhim, Rebecca <Rebecca.Beroukhim@CARDIO.CHBOSTON.ORG> Hello, I was hoping for help with the following 2 issues:
First issue: I’m trying to groom my dataset by aligning with landmarks but keep getting the following error (multiple errors throughout and then the app crashes). I have checked all my landmarks and they look good. I loaded a smaller number of particles and am still getting the same error.
[2024-08-31 11:41:10.159] [warning] Generic Warning: In C:\bdeps\vtk\Common\Core\vtkMath.cxx, line 814 vtkMath::Jacobi: Error extracting eigenfunctions
Second issue: In my analysis (prior to when the above error occurred), my first shape mode was significantly correlated with volume. Is it correct that if I enable Procrustes scaling, that this should remove any component of shape volume in the principle components analysis?
Thanks Rebecca S. Beroukhim MD |Department of Cardiology | Boston Children’s Hospital | 300 Longwood Avenue | Boston, MA 02115 | Phone 617-355-4521 |Fax 617-730-4791 | rebecca.beroukhim@cardio.chboston.org
Boston Children’s Hospital recommends that all patient communication is through the hospital portal: Boston Children’s Hospital MyChildrens: https://apps.childrenshospital.org/mychildrens/index.html
From: Alan Morris <alan.morris@utah.edu>
Hi,
The first mode of variation is the mode that explains the most variance within the model. It does not have to be absolute size, it depends on the dataset. In datasets where there are variations in total size/volume, we very often find that variation as the largest component of the first mode of variation.
Thanks, Alan
From:
Barnet, Isabel <ibarnet@hms.harvard.edu> Hi all!
As a quick addendum to this question, does p0 have a special significance (ie does it always represent the absolute size/volume of a shape in a model), or does it merely represent the mode explaining the most variance?
Thanks! Isabel
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