# Probabilistic 3D surface reconstruction from sparse MRI information

arXiv: https://arxiv.org/abs/2010.02041 · HTML: https://mlsanity.com/paper/2010.02041/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud](https://mlsanity.com/paper/2305.14486/) (2023)
- [Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI](https://mlsanity.com/paper/2311.13706/) (2023)
- [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)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Uncertainty modeling for fine-tuned implicit functions](https://mlsanity.com/paper/2406.12082/) (2024)
- [Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations](https://mlsanity.com/paper/2409.07100/) (2024)
- [From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2510.01296/) (2025)
- [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)
- [Surface-to-Skeleton 3D Cephalometry: Estimating Hidden Skeletal Landmarks from CT-Derived External Soft-Tissue Surfaces](https://mlsanity.com/paper/2608.12537/) (2026)

## Abstract (arXiv)

Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce shapes of limited variability positioned in a canonical position or lack uncertainty evaluation. In this paper, we present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets whilst modelling the location of each mesh vertex through a Gaussian distribution. Prior shape information is encoded using a built-in linear principal component analysis (PCA) model. Extensive experiments on cardiac MR data show that our probabilistic approach successfully assesses prediction uncertainty while at the same time qualitatively and quantitatively outperforms SOTA methods in shape prediction. Compared to SOTA, we are capable of properly localising and orientating the prediction via the use of a spatially aware neural network.