Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.

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_SSM) by introducing a nonlinear relationship between anatomical parameters and bone shape information.

Methods: Our approach utilizes a multilayer perceptron model trained on a synthetic femoral bone population to learn the nonlinear mapping between anatomical measurements and shape parameters. The trained model is then fine-tuned on a real bone dataset. We compare the performance of DL-ANAT_SSM with a linear ANAT_SSM generated using least-squares regression for baseline evaluation.

Results: When applied to a previously unseen femoral bone dataset, DL-ANAT_SSM demonstrated superior performance in predicting 3D bone shape based on anatomical parameters compared to the linear baseline model. The impact of fine-tuning was also investigated, with results indicating improved model performance after this process.

Conclusion: The proposed DL-ANAT_SSM is therefore a more precise and interpretable SSM, which is directly controlled by clinically relevant parameters. The proposed method holds promise for applications in both morphometry analysis and patient-specific 3D model generation without preoperative images.