Construction of Multi-resolution Multi-organ Shape Model Based on Stacked Autoencoder Neural Network

Abstract

The construction of statistical shape models (SSMs) is an important method in the field of medical image segmentation. Most SSMs are constructed by using traditional modeling methods based on principal component analysis (PCA), which cannot fully present the true deformation ability of models. To solve the insufficient deformation ability of SSMs, we propose a stacked autoencoder (SAE) neural network to construct a multi-resolution multi-organ shape model based on mouse micro-CT images, which can express more linear and non-linear deformations than SSMs based on PCA. The main advantage of this method is that the SAE neural network is simple and flexible and it can learn more deformation modes from training data. We have quantitatively compared the modeling performance of this method with the constructed SSMs based on PCA in terms of model generalization and specificity.