Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rasal:2023:10.1007/978-3-031-25075-0_28,
author = {Rasal, R and Castro, DC and Pawlowski, N and Glocker, B},
doi = {10.1007/978-3-031-25075-0_28},
pages = {400--432},
publisher = {Springer Nature Switzerland},
title = {Deep structural causal shape models},
url = {http://dx.doi.org/10.1007/978-3-031-25075-0_28},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Rasal,R
AU  - Castro,DC
AU  - Pawlowski,N
AU  - Glocker,B
DO  - 10.1007/978-3-031-25075-0_28
EP  - 432
PB  - Springer Nature Switzerland
PY  - 2023///
SN  - 0302-9743
SP  - 400
TI  - Deep structural causal shape models
UR  - http://dx.doi.org/10.1007/978-3-031-25075-0_28
UR  - https://link.springer.com/chapter/10.1007/978-3-031-25075-0_28
ER  -
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