Hi,

1 . In order to get repeatable results, i.e same exact correspondence model, you might want to disable openmp at the expense of longer computation time ... nonetheless, with openmp, you still get similar correspondence model in terms of representation, yet not the same exact ordering and positional information.

2. points in wpts are already procrustes aligned and can directly be fed to PCA. we are working on an improved version of SW and will add the exportation of the eigenmodes and scores ... thanks for bringing this up  ... for the time being, please refer to ComputeModeLengths.cxx for utilities that would compute pca modes and scores ...


hope this helps ...

best regards
Shireen

On 02/17/2016 12:07 PM, Murat Maga wrote:

Hi,

 

I want to follow up on this. We want to use the PC scores from the shapeworks analysis as input for our quantitative trait mapping to find genomic intervals associated with molar morphology and size. We successfully used this approach using traditional landmarks, but I definitely see value using SW. However, there are couple concerns:

 

1.      We are not getting repeatable results (as Ryan described below) with identical datasets and parameter xmls. Parameter space is too big to randomly experiment on our own. We can use suggestions on what to focus and what might be driving this randomness. 2.      Although I can visualize the modes of variation using SW Studio, I can’t get to the individual eigenvectors or the PC scores. To overcome this, I am feeding the wpts files into a Procrustes analysis and doing alignment and PCA, which is kind of silly, since SW does it already. It would be good to expose the PC scores an eigenvectors.

 

Best wishes,

Murat

 

 

A. Murat Maga, PhD

Assistant Professor

UW Dept. of Pediatrics

Division of Craniofacial Medicine

&

Member

Seattle Children’s Research Institute

Center for Developmental Biology and Regenerative Medicine

 

 

 

 

Hello,

I have been using shapeworks to landmark six different mouse molars.  Shapeworks was run twice on each molar, with identical parameters and volumes.  The results are surprisingly different.  Regressing the distance to the mean shape of each the landmarks configuration between runs, 5-10 samples are drastically different.  See attached plot.  I was wondering if I am running the samples with enough iterations?  I am going to experiment with increasing the number of iteration to see if the results stabilize, but would greatly appreciate any suggestions.  The top of the groom.xml and run.xml files are below.

groom.xml

<?xml version="1.0" encoding="UTF-8"?>
<blur_sigma>0.5</blur_sigma>
<antialias_iterations>50</antialias_iterations>
<background>0</background>
<foreground>2</foreground>
<transform_file>/cm/shared/data/n2_molars/shapeworks/label1//shapeworks.transform</transform_file>
<inputs>/cm/shared/data/n2_molars/shapeworks/label1/def_152-11_to_atlas.nii.gz_inv.nii.gz

run.xml
<?xml version="1.0" encoding="UTF-8"?>
<iterations_per_split>100</iterations_per_split>
<starting_regularization>1000</starting_regularization>
<ending_regularization>0.1</ending_regularization>
<number_of_particles>1024</number_of_particles>
<optimization_iterations>1000</optimization_iterations>
<output_points_prefix>/cm/shared/data/n2_molars/shapeworks/label1//output_points</output_points_prefix>
<output_transform_file>/cm/shared/data/n2_molars/shapeworks/label1//output_transform_file</output_transform_file>
<inputs>/