How to Add New Use Cases?

Adding a new use case entails the following.

  • Python-scripted workflow
  • Use case dataset
  • Use case documentation

Python-scripted workflow

To add a new use case to the codebase:

  • Add <use-case-name>.py python script to Examples/Python/, where the <use-case-name> is a descriptive name for the use case, typically associated with the class of shapes to be modeled (e.g., anatomy name)
  • Update Examples/Python/RunUseCase.py to add the new use case to the list of cases. Look for the line parser.add_argument("--use_case" ...). Make sure to use the <use-case-name> to the use case list.
  • Each use case needs to include the option to run a tiny test, which typically runs on only the first three shapes.
  • Each use case needs to have the option to run on a representative subsample of the dataset of any size. This subsample is selected via clustering on the shapes and selecting one shape from each cluster.

Use case dataset

To add the dataset associated with the new use case:

Use case documentation

To add the documentation of the new dataset, add a markdown file <use-case-name>.md in docs/use-cases with the following structure. Please use the Naming and Organization conventions currently deployed for ShapeWorks documentation. Then, add a corresponding entry in mkdocs.yml.

Important

If you use the documentation of an existing use case as a starting point, PLEASE make sure to adapt the documentation to your new use case.

Important

PLEASE, use a spell and grammar checker (e.g., grammarly).

  • What and Where is the Use Case?: The goal of this use case, i.e., what does this use case demonstrate about ShapeWorks? Details about the dataset, e.g., number of samples, class of shapes, shape representation (meshes, segmentation, or contours), dataset source ... etc. Link to the use case python script.
  • Running the Use Case: Instructions to run the use case that highlights the specific use of RunUseCase.py (in Examples/Python/) tags specifically for this use case.
  • Grooming Data: Details about the data preprocessing pipeline for this use case that highlights specific considerations for this use case. Snapshots of intermediate and final groom outputs for illustration.
  • Optimizing Shape Model: Default optimization parameters used for this use case, including a discussion of intuitions/insights on how these parameters were chosen.
  • Analyzing Shape Model: Videos and snapshots of the optimized shape model(s), including the mean shape, scree plot, modes of variations, and individual samples. When applicable, also include videos and snapshots of results that are specific to the use case (e.g., group differences, linear discriminant analysis ... etc.).