Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.
Journal:
International journal of computer assisted radiology and surgery
PMID:
39953355
Abstract
PURPOSE: Statistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical parameters. To address this limitation, this paper proposes a novel deep learning-based anatomically parameterized SSM (DL-ANAT) by introducing a nonlinear relationship between anatomical parameters and bone shape information.