# Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning

## TL;DR

This paper reviews methods for modeling how biological shapes change over time, using techniques from diffeomorphic mapping to deep learning.

## Contribution

The paper provides a comprehensive review of spatiotemporal shape modeling techniques and identifies gaps for future research.

## Key findings

- Longitudinal shape modeling is crucial for diagnostic and therapeutic applications.
- Diffeomorphic metric mapping and deep learning approaches are key tools in this field.
- Current methods have limitations that need addressing for more accurate modeling.

## Abstract

Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.