DeepAI AI Chat
Log In Sign Up

Probabilistic 3D surface reconstruction from sparse MRI information

by   Katarína Tóthová, et al.

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.


page 7

page 8

page 14


Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Surface reconstruction is a vital tool in a wide range of areas of medic...

A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps

With the advent of NASA's lunar reconnaissance orbiter (LRO), a large am...

A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction

Automated construction of surface geometries of cardiac structures from ...

Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation

Multi-parametric magnetic resonance (MR) imaging is an indispensable too...

CorticalFlow^++: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability

The problem of Cortical Surface Reconstruction from magnetic resonance i...

A Simple and Scalable Shape Representation for 3D Reconstruction

Deep learning applied to the reconstruction of 3D shapes has seen growin...

Functional Data Analysis and Visualisation of Three-dimensional Surface Shape

The advent of high resolution imaging has made data on surface shape wid...