Shape Model for Multiple Domains

What and Where is the Use Case?

This use case demonstrates using ShapeWorks tools to perform the following.

  • Generate shape models for anatomies with multiple structures (domains), e.g., joints.
  • Optimization is carried out in the given domains' shared/joint shape spaces to capture inter-domains correlations and interactions

The use case is located at: Examples/Python/ellipsoid_multiple_domain.py

There are three datasets available for this use case: - ellipsoid_joint_rotation - ellipsoid_joint_size - ellipsoid_joint_size_rotation By defualt, the use case runs on the ellipsoid_joint_rotation dataset. The name of the dataset can be changed in the ellipsoid_multiple_domain.py file if required.

Running the Use Case

To run the use case, run RunUseCase.py (in Examples/Python/).

$ cd /path/to/shapeworks/Examples/Python
$ python RunUseCase.py ellipsoid_multiple_domain

This calls ellipsoid_multiple_domain.py (in Examples/Python/) to perform the following.

  • Loads the ellipsoid joint dataset using a local version if it exists (i.e., previously downloaded); otherwise, the dataset is automatically downloaded from the ShapeWorks Data Portal.
  • Optimizes particle distribution (i.e., the shape/correspondence model) by calling optimization functions in OptimizeUtils.py (in Examples/Python/). See Optimizing Shape Model for details about algorithmic parameters for optimizing the shape model.
  • Launches ShapeWorks Studio to visualize the use case results (i.e., the optimized shape model and the groomed data) by calling functions in AnalyzeUtils.py (in Examples/Python/).

Grooming Data

In this use case, we download pre-aligned data. Hence we require only two groooming steps. 1. Isotropic Resampling: Binary segmentations in ellipsoid_joint_rotation/segmentations/ are resampled to have an isotropic voxel spacing. 2. Distance Transform: Next,the smooth signed distance transform is computed, and the dataset is now ready for the optimize phase. The use case will be updated soon to demonstrate the full grooming process including alignment.

Optimizing Shape Model

Below are the default optimization parameters when running this use case. For a description of the optimize tool and its algorithmic parameters, see: How to Optimize Your Shape Model.

$ python RunUseCase.py ellipsoid_multiple_domain
The list of <inputs> (binary segmentation images) should be ordered consistently for each shape.(e.g., shape1-domain1, shape1-domain2, shape2-domain1, shape2-domain2 ... etc.).

{
        "number_of_particles" : [512,512],
        "use_normals": [0,0],
        "normal_weight": [1.0,1.0],
        "checkpointing_interval" : 200,
        "keep_checkpoints" : 0,
        "iterations_per_split" : 500,
        "optimization_iterations" : 500,
        "starting_regularization" :100,
        "ending_regularization" : 0.5,
        "recompute_regularization_interval" : 2,
        "domains_per_shape" : 2,
        "domain_type" : 'image',
        "relative_weighting" : 1, 
        "initial_relative_weighting" : 0.1,
        "procrustes_interval" : 0,
        "procrustes_scaling" : 0,
        "save_init_splits" : 0,
        "verbosity" : 3

      }

Analyzing Shape Model

ShapeWorks Studio visualizes/analyzes the optimized particle-based shape model by visualizing the mean shape, individual shape samples, and the shape modes of variations. For more information, see: How to Analyze Your Shape Model?.