Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Katarína Tóthová*, Sarah Parisot, Matthew C.H. Lee, Esther Puyol-Antón, Lisa M. Koch, Andrew P. King, Ender Konukoglu, Marc Pollefeys

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

9 Citations (Scopus)


Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the reconstruction problem, most, due to their deterministic nature, do not directly address the issue of quantifying uncertainty associated with their predictions. We remedy this by proposing a novel probabilistic deep learning approach capable of simultaneous surface reconstruction and associated uncertainty prediction. The method incorporates prior shape information in the form of a principal component analysis (PCA) model. Experiments using the UK Biobank data show that our probabilistic approach outperforms an analogous deterministic PCA-based method in the task of 2D organ delineation and quantifies uncertainty by formulating distributions over predicted surface vertex positions.


  • Deep learning
  • Shape prior
  • Surface reconstruction
  • Uncertainty quantification


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