Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Abstract
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.
Cite
@article{arxiv.1807.11272,
title = {Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors},
author = {Katarína Tóthová and Sarah Parisot and Matthew C. H. Lee and Esther Puyol-Antón and Lisa M. Koch and Andrew P. King and Ender Konukoglu and Marc Pollefeys},
journal= {arXiv preprint arXiv:1807.11272},
year = {2018}
}
Comments
Accepted to ShapeMI MICCAI 2018: Workshop on Shape in Medical Imaging