- Conference Paper
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. Show more
Book titleMedical Image Computing and Computer Assisted Intervention – MICCAI 2020
Journal / seriesLecture Notes in Computer Science
Pages / Article No.
SubjectUncertainty quantification; 3D reconstruction; Shape modelling; Deep learning
Organisational unit03766 - Pollefeys, Marc / Pollefeys, Marc
09579 - Konukoglu, Ender / Konukoglu, Ender
NotesConference lecture on October 6, 2020. Due to the Corona virus (COVID-19) the conference was conducted virtually.
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