Fully Bayesian VIB-DeepSSM
This work addresses the need for calibrated uncertainty in deep learning-based statistical shape modeling for clinical diagnosis, representing an incremental improvement over prior methods.
The paper tackled the problem of predicting statistical shape models from unsegmented 3D images with improved uncertainty quantification by developing a fully Bayesian VIB-DeepSSM framework, resulting in enhanced uncertainty reasoning without loss of accuracy as demonstrated on synthetic shapes and left atrium data.
Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-DeepSSM) is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. However, VIB is only half-Bayesian and lacks epistemic uncertainty inference. We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble. Additionally, we introduce a novel combination of the two that further enhances uncertainty calibration via multimodal marginalization. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy.