Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

TL;DR

This paper introduces a probabilistic deep learning method for surface reconstruction in medical imaging that incorporates shape priors and provides uncertainty quantification, improving accuracy over deterministic approaches.

Contribution

It presents a novel probabilistic CNN approach that integrates PCA shape priors for surface prediction and uncertainty estimation in medical image analysis.

Findings

Outperforms deterministic PCA-based methods in organ delineation tasks.

Quantifies uncertainty through distributions over surface vertices.

Demonstrates effectiveness on UK Biobank data.

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.