Rigid motion invariant statistical shape modeling based on discrete fundamental forms: Data from the osteoarthritis initiative and the Alzheimer's disease neuroimaging initiative.
Med Image Anal
; 73: 102178, 2021 10.
Article
in En
| MEDLINE
| ID: mdl-34343840
ABSTRACT
We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we outperform state-of-the-art classifiers based on geometric deep learning as well as statistical shape modeling especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Osteoarthritis
/
Alzheimer Disease
Type of study:
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Med Image Anal
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2021
Document type:
Article