# Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

arXiv: https://arxiv.org/abs/1807.11272 · HTML: https://mlsanity.com/paper/1807.11272/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach](https://mlsanity.com/paper/2205.06862/) (2022)
- [Fully Bayesian VIB-DeepSSM](https://mlsanity.com/paper/2305.05797/) (2023)
- [Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions](https://mlsanity.com/paper/2305.11946/) (2023)
- [ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images](https://mlsanity.com/paper/2307.03273/) (2023)
- [Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images](https://mlsanity.com/paper/2405.09697/) (2024)
- [Quantification of Predictive Uncertainty via Inference-Time Sampling](https://mlsanity.com/paper/2308.01731/) (2023)
- [Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images](https://mlsanity.com/paper/2407.01931/) (2024)

## Abstract (arXiv)

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.