No. | Content |
---|---|
1 | 【ShapeWorks】1. 软件介绍及安装 |
2 | 【ShapeWorks】2. 工作流的三板斧 - How to Groom Your Dataset? |
3 | 【ShapeWorks】3. 工作流的三板斧 - How to Optimize Your Shape Model? |
4 | 【ShapeWorks】4. 工作流的三板斧 - How to Analyze Your Shape Model? |
5 | 【ShapeWorks】5. 典型例子 Ellipsoid 的运行及解析 |
This example is a stepping stone for the user to get familiar with the workflow of ShapeWorks. This use case represents the standard ShapeWorks workflow on a synthetically generated ellipsoid dataset, including the entire grooming process.
This is how the segmentations in the dataset look before grooming. Here it can been seen that the ellipsoids have random centers and n orientations.
ellipsoid/segmentations/
are resampled to have an isotropic voxel spacing.Distance transform obtained after grooming.Here we show how the shapes would look like if the transforms are applied.Distance transform obtained after grooming
The python code for the use case calls the optimize
command of ShapeWorks which reads the project sheet with the shape filenames and optimization parameter values. See Project excel file for details regarding creating the project sheet. Below are the default optimization parameters for this use case.
{
"number_of_particles": 128,
"use_normals": 0,
"normals_strength": 10.0,
"checkpointing_interval": 1000,
"keep_checkpoints": 0,
"iterations_per_split": 1000,
"optimization_iterations": 1000,
"starting_regularization": 10,
"ending_regularization": 1,
"recompute_regularization_interval": 1,
"domains_per_shape": 1,
"relative_weighting": 1,
"initial_relative_weighting": 0.05,
"procrustes_interval": 0,
"procrustes_scaling": 0,
"save_init_splits": 0,
"verbosity": 0
}
Once the python code runs the opimization, the files are saved in the Output
folder after which ShapeWorks Studio is launched from the python code to analyze the model.
Below is the mean shape resulting from optimization. Here we can see that there is only one major mode of variation.
Below are the particles for each sample, we can see they are uniformly distributed.
Animating along the first PCA mode we can see the variation in the radius along the x-axis.
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