Model-order selection in statistical shape models

TL;DR

This paper introduces an information-theoretic method for selecting the optimal model order in statistical shape models, improving the balance between overfitting and underfitting in segmentation tasks.

Contribution

It proposes a novel technique for model order selection in Point Distribution Models based on information criteria, addressing limitations of previous percentage-based methods.

Findings

The proposed method effectively balances model complexity and data fit.

Empirical results show improved segmentation accuracy.

The technique adapts to training data size and noise levels.

Abstract

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.