21 research outputs found
Probabilistic 3D surface reconstruction from sparse MRI information
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.Comment: MICCAI 202
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
Image registration is an ill-posed inverse problem which often requires
regularisation on the solution space. In contrast to most of the current
approaches which impose explicit regularisation terms such as smoothness, in
this paper we propose a novel method that can implicitly learn
biomechanics-informed regularisation. Such an approach can incorporate
application-specific prior knowledge into deep learning based registration.
Particularly, the proposed biomechanics-informed regularisation leverages a
variational autoencoder (VAE) to learn a manifold for biomechanically plausible
deformations and to implicitly capture their underlying properties via
reconstructing biomechanical simulations. The learnt VAE regulariser then can
be coupled with any deep learning based registration network to regularise the
solution space to be biomechanically plausible. The proposed method is
validated in the context of myocardial motion tracking on 2D stacks of cardiac
MRI data from two different datasets. The results show that it can achieve
better performance against other competing methods in terms of motion tracking
accuracy and has the ability to learn biomechanical properties such as
incompressibility and strains. The method has also been shown to have better
generalisability to unseen domains compared with commonly used L2
regularisation schemes.Comment: The paper is early accepted by MICCAI 202