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
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:
Running the Use Case
To run the use case, run
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
Examples/Python/) to perform the following.
- Loads the ellipsoid dataset using a local version if it exists (i.e., previously downloaded); otherwise, the dataset is automatically downloaded from the ShapeWorks Data Portal.
- Grooms the segmentations by calling data preprocessing functions 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
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
If you wish to start with the optimization step using a previously groomed data, add
$ python RunUseCase.py --use_case ellipsoid_cut --start_with_prepped_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?.
- 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.
- 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.
- 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.
- 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).
- Rigid Alignment: All of the segmentations are then aligned to the selected reference using rigid alignment, which factors out the rotation and remaining translation.
- Cropping: The segmentations are cropped so that all of the samples are within the same bounding box.
- 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
Examples/Python/). Also note the use of
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, "adaptivity_mode": 0, "cutting_plane_counts": cutting_plane_counts, "cutting_planes": cutting_planes
Analyzing Shape Model
ShapeWorksStudio 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?.