# Ellipsoid: Cutting Planes

## What and Where is the Use Case?

The ellipsoid dataset comprises of axis-aligned ellipsoids with varying radii along a single axis. This example demonstrates using multiple cutting planes to constrain the distribution of particles. This can be used in modeling scenarios where statistical modeling/analysis is needed for a region-of-interest on the anatomy/object-class at hand without having to affect the input data.

Ellipsoids (major radius as one mode of variation) with 2 cutting planes

The ellipsoid_cut.py (in Examples/Python/) use case represents the standard use version of a shape modeling workflow that entails one or more cutting planes using ShapeWorks. It includes the full pipeline for processed (i.e., groomed) as well as unprocessed data.

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

## Running the Use Case

To run the use case, run RunUseCase.py (in Examples/Python/) with proper tags. The tags control the type of input data and the optimization method. See Getting Started with Use Cases for the full list of tags.

• --skip_grooming: to run the optimization on previously processed/groomed data
• --use_single_scale: to use the single-scale optimization. Default is multi-scale optimization

To run the full pipeline with multi-scale:

$cd /path/to/shapeworks/Examples/Python$python RunUseCase.py --use_case ellipsoid_cut


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

• Grooms the segmentations by calling data preprocessing functions in GroomUtils.py (in Examples/Python/). See Grooming Data for details about these preprocessing steps.
• Defines two cutting planes to be used to constrain the particle optimization on all ellipsoid. Note that this dataset contains a set of roughly aligned ellispoids; hence a common set of cutting planes can be used for all samples.
    cutting_plane_points1 = [[10, 10, 0], [-10, -10, 0], [10, -10, 0]]
cutting_plane_points2 = [[10, -3, 10], [-10, -3 ,10], [10, -3, -10]]
cp = [cutting_plane_points1, cutting_plane_points2]

• 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 ShapeWorksStudio to visualize the use case results (i.e., optimized shape model and the groomed data) by calling functions in AnalyzeUtils.py (in Examples/Python/).

If you wish to start with the optimization step using a previously groomed data, add --start_with_prepped_data tag.

\$ python RunUseCase.py --use_case ellipsoid_cut --start_with_prepped_data


## Grooming Data

The following preprocessing steps are only performed when you start with unprepped data, i.e., the tag --start_with_prepped_data is not used. For a description of the grooming tools and parameters, see: How to Groom Your Dataset?.

1. Isotropic Resampling: Binary segmentations in ellipsoid/segmentations/ are resampled to have an isotropic voxel spacing using a user-defined spacing. This step could also be used to produce segmentations with smaller voxel spacing, and thereby reduce aliasing artifacts (i.e., staircase/jagged surface) due to binarization.
2. Apply Padding: Segmentations that touch the image boundary will have an artificial hole at that intersection. Segmentations are padded by adding a user-defined number of voxels along each image direction (rows, cols, and slices) to avoid introducing artificial holes.
3. Center-of-Mass Alignment: This translational alignment step is performed before rigidly aligning the samples to a shape reference. This factors out translations to reduce the risk of misalignment and allow for a medoid sample to be automatically selected as the reference for rigid alignment.
4. Reference Selection: The reference is selected by first computing the mean (average) distance transform of the segmentations, then selecting the sample closest to that mean (i.e., medoid).
5. Rigid Alignment: All of the segmentations are then aligned to the selected reference using rigid alignment, which factors out the rotation and remaining translation.
6. Cropping: The segmentations are cropped so that all of the samples are within the same bounding box.
7. Distance Transform: Finally, the signed distance transform is computed, and the dataset is now ready for the optimize phase.

## Optimizing Shape Model

Below are the default optimization parameters for this use case. For a description of the optimize tool and its algorithmic parameters, see: How to Optimize Your Shape Model. Note that use_shape_statistics_after parameter is not used when --use_single_scale tag is given to the RunUseCase.py (in Examples/Python/). Also note the use of adaptivity_mode, cutting_plane_counts, and cutting_planes optimization parameters to trigger the constrained particles optimization.

        "number_of_particles": 128,
"use_normals": 1,
"normal_weight": 10.0,
"checkpointing_interval": 200,
"keep_checkpoints": 0,
"iterations_per_split": 2000,
"optimization_iterations": 1000,
"starting_regularization": 100,
"ending_regularization": 10,
"recompute_regularization_interval": 2,
"domains_per_shape": 1,
"domain_type": 'image',
"relative_weighting": 10,
"initial_relative_weighting": 0.01,
"procrustes_interval": 0,
"procrustes_scaling": 0,
"save_init_splits": 0,
"verbosity": 2,