Deep Structural Causal Shape Models

TL;DR

This paper introduces deep structural causal shape models (CSMs) that enable causal reasoning and counterfactual generation of 3D anatomical shapes, advancing personalized medical prognosis beyond population-level analysis.

Contribution

The paper presents a novel framework combining geometric deep learning and causal modeling to generate subject-specific counterfactual anatomical shapes.

Findings

CSMs can generate realistic counterfactual 3D brain structures.

The models demonstrate causal reasoning at all levels of Pearl's hierarchy.

Experiments show improved personalized prognosis capabilities.

Abstract

Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may want to study the causal effect of genetic, environmental, or lifestyle factors on the normal and pathological variation of anatomical phenotypes. However, while anatomical shape models of 3D surface meshes, extracted from automated image segmentation, can be reliably constructed, there is a lack of computational tooling to enable causal reasoning about morphological variations. To tackle this problem, we propose deep structural causal shape models (CSMs), which utilise high-quality mesh generation techniques, from geometric deep learning, within the expressive framework of deep structural causal models. CSMs enable subject-specific prognoses through counterfactual mesh generation ("How would this patient's brain structure change if they were ten years older?"), which is in contrast to most current works on purely population-level statistical shape modelling. We demonstrate the capabilities of CSMs at all levels of Pearl's causal hierarchy through a number of qualitative and quantitative experiments leveraging a large dataset of 3D brain structures.