ModelMetrics

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9. def generalization(pcaModel: StatisticalMeshModel, dc: DataCollection): Try[Double]

   

   Returns the generalization metric of the Statistical Mesh Model, that is how well the model can represent unseen data

   Returns the generalization metric of the Statistical Mesh Model, that is how well the model can represent unseen data

   pcaModel

   Statistical Mesh Model to be evaluated

   dc

   test data collection that is in correspondence with the model reference The implementation of this metric is inspired from : Styner, Martin A., et al. "Evaluation of 3D correspondence methods for model building." Information processing in medical imaging. Springer Berlin Heidelberg, 2003. For every mesh in the test data, we project the mesh into the model (that is find the closest shape in the model space to the given mesh) and compute the average mesh distance (see scalismo.mesh.MeshMetrics) between the mesh and the projection. To be able to perform the projection, it is important that the data collection is in correspondence with the model. The returned value is a scala.util.Try containing the average over all test data in case of success, or an Exception otherwise

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16. def specificity(pcaModel: StatisticalMeshModel, data: Iterable[TriangleMesh], nbSamples: Int): Double

    

    Returns the specificity metric of the Statistical Mesh Model, that is how close the model remains to the category of shapes it is supposed to represent

    Returns the specificity metric of the Statistical Mesh Model, that is how close the model remains to the category of shapes it is supposed to represent

    pcaModel

    model to be evaluated

    data

    test data to verify specificity against

    nbSamples

    number of samples drawn to compute the average The implementation of this metric is inspired from : Styner, Martin A., et al. "Evaluation of 3D correspondence methods for model building." Information processing in medical imaging. Springer Berlin Heidelberg, 2003. The general idea is as follows : 1 - sample a shape from the mesh model 2- compute the average mesh distance (see scalismo.mesh.MeshMetrics) of the sample to all elements of the given sequence of meshes and select the minimum distance These steps are then repeated nbSamples times and the average value is returned.

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